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Face Datasets facial recognition and analysis Datage

Face Recognition Homepage - Database

  1. 2D-3D-face-datasets 2D WIDER FACE NIST-provided (IJB-B + FRVT (testing only)) AFW 300-W (LFPW + AFW + HELEN + IBUG) MALF FDDB (2009/10) VGGFace VGGFace2 3D Landmarks MEMPO LS3D-W FLORENCE 3D/2D (acquired) FLORENCE superface DMCSv1 (Multimodal Biometric Database of 3D Face and Hand Scans) 3D Mask Attack Database (3DMAD) EURECOM Kinect Face.
  2. Tufts Face Database is the most comprehensive, large-scale face dataset that contains 7 image modalities: visible, near-infrared, thermal, computerised sketch, LYTRO, recorded video, and 3D images. Size: The dataset contains over 10,000 images, where 74 females and 38 males from more than 15 countries with an age range between 4 to 70 years old.
  3. The WIDER FACE dataset is a face detection benchmark dataset. It consists of 32.203 images with 393.703 labelled faces with high variations of scale, pose and occlusion. This data set contains the annotations for 5171 faces in a set of 2845 images taken from the well-known Faces in the Wild (LFW) data set.
  4. Dataset of 8364 restaurant reviews scrapped from qaym.com in Arabic for sentiment analysis. ar_sarcasm. ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them
Lesson 07 - Scikit-Learn

The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large. Downloading this dataset implies agreement to follow the same conditions of non-commercial research for any modification and/or re-distribution of the dataset in any form. How to use DiveFace: Download Megaface training set (Tightly Cropped - Face detection box region only). Meface is property of the University of Washington

racy, which limits the applicability of face analytic systems to non-White race groups. To mitigate the race bias prob-lem in these datasets, we constructed a novel face image dataset containing 108,501 images which is balanced on race. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Im Face Databases From Other Research Groups . We list some face databases widely used for face related studies, and summarize the specifications of these databases as below. 1. Caltech Occluded Face in the Wild (COFW). o Source: The COFW face dataset is built by California Institute of Technology

The use of dataset for face recognition usually uses images of photos originated from single media such as dataset from mobile phone [1,2], Facebook , digital camera [4,5]. Algorithm development for face recognition requires images dataset from various media sources, it is a challenge for researchers because the expected results in face. The Non-Treachery of Dataset. ArtGAN and WikiArt: Making Art with AI. Making AI with Art. we argue that classification of fine-art collections is a more challenging problem in comparison to objects or face recognition. This is because some of the artworks are non-representational nor figurative, and might requires imagination to recognize.

Databases for Face Detection - NCK

  1. Samples from SoF dataset: metadata for each image includes 17 facial landmarks, a glass rectangle, and a face rectangle. It looks like glasses as a natural occlusion threaten the performance of.
  2. The IJB-C dataset is a video-based face recognition dataset. It is an extension of the IJB-A dataset with about 138,000 face images, 11,000 face videos, and 10,000 non-face images. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results
  3. Datasets. code. Code. comment. Discussions. school. Courses. expand_more. More. auto_awesome_motion. 0. View Active Events. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies. Got it. Learn more
  4. CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. It has substantial pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary.
  5. Introduction. The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects. Thus, there is large variation in pose, lighting, expression, scene.
  6. In all, 5,080 images containing 28,231 faces are labeled with age and gender, making this what we believe is the largest dataset of its kind. Many faces have low resolution. The median face has only 18.5 pixels between the eye centers, and 25% of the faces have under 12.5 pixels. As is expected with Flickr images, there is a great deal of variety
  7. Makeup Datasets is a series of datasets of female face images assembled for studying the impact of makeup on face recognition. MIW (Makeup in the Wild) Dataset - There is one set of data, Makeup in the Wild that contains face images of subjects with and without makeup that were obtained from the internet. There are 154 images of 125.

CMU Face Images Data Set. Download: Data Folder, Data Set Description. Abstract: This data consists of 640 black and white face images of people taken with varying pose (straight, left, right, up), expression (neutral, happy, sad, angry), eyes (wearing sunglasses or not), and size. Data Set Characteristics: Image. Number of Instances: 640. Area Face Mask Detection Dataset 7553 Images. Face Mask Detection Data set In recent trend in world wide Lockdowns due to COVID19 outbreak, as Face Mask is became mandatory for everyone while roaming outside, approach of Deep Learning for Detecting Faces With and Without mask were a good trendy practice The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age.

GitHub - blancaag/face-dataset

  1. HuggingFace Datasets¶. Datasets and evaluation metrics for natural language processing. Compatible with NumPy, Pandas, PyTorch and TensorFlow. Datasets is a lightweight and extensible library to easily share and access datasets and evaluation metrics for Natural Language Processing (NLP)
  2. For Masked face recognition work, i need a masked face dataset with classification labeling. where test/train/valid folder exist and each folder contain different persons subfolder with images
  3. als and non-cri
  4. The most covert dataset available to date Details: • Contains 6,337 visible images from 308 subjects • Images captured at a distance of roughly 100m • Dataset is ideal for re-identification scenario Sapkota, Archana, and Terrance E. Boult. Large scale unconstrained open set face database. Biometrics: Theory
  5. Smile Recognition Using OpenCV and scikit-learn. In this post, we're going to dab a little bit in machine learning and face recognition to predict if an image from a live webcam shows a smiling subject or not. First, we will use an existing dataset, called the Olivetti faces dataset and classify the 400 faces seen there in one of two.
  6. Magichub is an open data platform where you can find datasets in multiple languages. Use the diverse scenes on MagicHub, meeting the needs of your AI model
  7. Evaluation Of A Non-Face To Face Multidisciplinary Health Care Model In A Population With Rheumatoid Arthritis Vulnerable To Covid-19 In A Health Emergency Situation. Published: 27-07-2021 Dataset metrics. Institutions. Fundacion Universitaria de Ciencias de la Salud. Categories

10 Face Datasets To Start Facial Recognition Project

Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of. Face Detection and Recognition Dataset with over 70000 faces and 1700 identities spread over 25000 images. 2 nd Unconstrained Face Detection and Open Set Recognition Challenge. Addressing concerns from the non research community. In the news there have been several discussions and published articles about this dataset. Majority of these. Red is non-face and blue is face data Result of color segmentation using Global thresholding Overlap exists in RGB space also Sample Blue vs Green plot for face (blue) and non-face (red) data. Superimposed mask image with eroded regions for estimate of centroids Mean Face used for template matching 2-D weighting function Sample correlation. Nonlinear 3D Face Morphable Model. As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, including model fitting, image synthesis, etc. Conventional 3DMM is learned from a collection of wellcontrolled 2D face images with associated 3D face scans, and represented by two sets. Face-to-face communication networks: networks of face-to-face (non-online) interactions; Graph classification datasets: disjoint graphs from different classes; SNAP networks are also available from SuiteSparse Matrix Collection by Tim Davis. Social network

Dataset consists of 400 faces Extracting the top 6 Eigenfaces - PCA using randomized SVD... done in 0.049s Extracting the top 6 Non-negative components - NMF... done in 0.109s Extracting the top 6 Independent components - FastICA... done in 0.181s Extracting the top 6 Sparse comp. - MiniBatchSparsePCA... done in 0.650s Extracting the top 6 MiniBatchDictionaryLearning... done in 0.456s. Let's create a dataset class for our face landmarks dataset. We will read the csv in __init__ but leave the reading of images to __getitem__. This is memory efficient because all the images are not stored in the memory at once but read as required. Sample of our dataset will be a dict {'image': image, 'landmarks': landmarks} The directory structure is: subject_name\video_number\video_number.frame.jpg For each person in the database there is a file called subject_name.labeled_faces.txt The data in this file is in the following format: filename,[ignore],x,y,width,height,[ignore],[ignore] where: x,y are the center of the face and the width and height are of the. The ND-IIITD Retouched Faces database is a dataset of original face images and retouched versions of those face images. The database contains 2600 original images and 2275 altered images. It is meant for use in the problem of developing methods to classify a face image as original or retouched The FaceTracer database is a large collection of real-world face images, collected from the internet. Each of the 15,000 faces in the database has a variety of metadata and fiducial points marked. In addition, a large subset of the faces contain hand-labeled descriptive attributes, including demographic information such as age and race, facial features like mustaches and hair color, and other.

low object confidence scores while training yolov2 for face-non face dataset. Ask Question Asked 1 year, 5 months ago. Active 1 year, 5 months ago. Viewed 531 times 1 Currently i am working on an object detection problem which involves detecting faces in an images and creating boxes around them. To address this issue , i have created a yolov2. Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension

Large face datasets are important for advancing face recogni-tion research, but they are tedious to build, because a lot of non-faces) found by the im-perfect face detector, and a number of them belong to other people appearing in the same image as the queried person or to people in images irrelevant to the query. We refer to thes 3D Face Dataset. 3D face datasets are of great value in face-related research areas. Existing 3D face datasets could be categorized according to the acquisition of 3D face model. Model fitting datasets[33, 60, 23, 5, 7] fit the 3D morphable model to the collected images, which makes it convenient to build a large-scale dataset on the base of. This used an artificial way to create a dataset by including a mask on a non-masked person image. Still, those images were not again used in the artificial generation process. The use of non-face mask samples involved the risk of the model becoming heavily biased. It was a risk to use such dataset images from various other sources The LFW (Labeled Faces in the Wild) dataset contains face photographs designed for studying the challenges of unconstrained face recognition. This database was created and is being maintained by the University of Massachusetts, Amherst faculty and researchers. Appproximately 13k images of ~6k people were detected and centered by the Viola Jones face detector The positive training dataset of 6,713 cropped 36x36 faces are provided from the Caltech Web Faces project. I used SIFT-like Histogram of Gradients(HoG) features to represent faces and non-faces images. To improve the performance, I flipped images left to right and obtained extra positive features from those mirrored face images

3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction. Yuda Qiu 1,2 Xiaojie Xu 2 Lingteng Qiu 1,2 Yan Pan 1,2 Yushuang Wu 1,2 Weikai Chen 3 Xiaoguang Han# 1,2* * Corresponding email: hanxiaoguang@cuhk.edu.cn 1 The Chinese University of Hong Kong, Shenzhen 2 Shenzhen Research Institute of Big Data 3 Tencent Game AI Research Cente Welcome to the Face Detection Data Set and Benchmark (FDDB), a data set of face regions designed for studying the problem of unconstrained face detection. This data set contains the annotations for 5171 faces in a set of 2845 images taken from the Faces in the Wild data set. More details can be found in the technical report below. Original.

Face Detection Datasets & Databases - facial finding

Microsoft Celeb. Microsoft Celeb (MS-Celeb-1M) is a dataset of 10 million face images harvested from the Internet for the purpose of developing face recognition technologies. According to Microsoft Research, who created and published the dataset in 2016, MS Celeb is the largest publicly available face recognition dataset in the world. The DFDC dataset is by far the largest currently- and publicly-available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods FaceScrub Face Dataset The FaceScrub dataset is a real-world face dataset comprising 107,818 face images of 530 male and female celebrities detected in images retrieved from the Internet. The images are taken under real-world situations (uncontrolled conditions). Name and gender annotations of the faces are included. Depth-Based Person.

Datasets - Hugging Fac

The IARPA Janus Benchmark-C face challenge (IJB-C) defines eight challenges addressing verification, identification, detection, clustering, and processing of full motion videos. This is supported by the IJB-C set of 138000 face images, 11000 face videos, and 10000 non-face images. Dataset Request Page Challenge Documentatio Description: CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of.

The UTKFace dataset is available for non-commercial research purposes only. The copyright belongs to the original owners. The LFW dataset Labeled Faces in the Wild is a public benchmark for face verification. The Large Age Gap Face Verification Dataset @article{bianco2017large-age, author = {Bianco, Simone}, year = {2017}, pages = {36-42} The IJB-B dataset is an extension of IJB-A, having 1, 845 subjects with 21.8 K still images (including 11, 754 face and 10, 044 non-face) and 55 K frames from 7, 011 videos. We evaluate the models on the standard 1:1 verification protocol (matching between the Mixed Media probes and two galleries) and 1:N identification protocol (1:N Mixed.

[2008.08016] MaskedFace-Net -- A Dataset of Correctly ..

tal of 21,798 (11,754 face and 10,044 non-face1) still im-ages, with an average of ˘6 face images/subject, and 55,026 video frames pulled from 7,011 full motion videos, with an average of ˘30 frames/subject and ˘4 videos/subject. While the dataset is still biased towards subjects from North America and India, IJB-B contains a more uniform geo Multivariate, Text, Domain-Theory . Classification, Clustering . Real . 2500 . 10000 . 201 A second dataset of 96,534 hands cropped from the Danbooru2019 SFW dataset using the PALM YOLO model. Hand detection can be used to clean images (eg remove face images with any hands in the way), or to generate datasets of just hands (as a form of data augmentation for GANs), to generate reference datasets for artists, or for other purposes FACEMETA - Hominological Face Dataset With Image Metadata The FACEMETA dataset is intended for use in academic research and corporate R&D. It contains 100,000 normalized photographs of male and female faces of varying ethnicity between the ages of 18 and 80

Description. In order to facilitate the study of age and gender recognition, we provide a data set and benchmark of face photos. The data included in this collection is intended to be as true as possible to the challenges of real-world imaging conditions. In particular, it attempts to capture all the variations in appearance, noise, pose. Very recently, researchers from Google [17] used a massive dataset of 200 million face identities and 800 million image face pairs to train a CNN similar to [28] and [18]. A point of difference is in their use of a triplet-based loss, where a pair of two congruous (a;b)and a third incongruous face c are compared MIFS: We assembled a dataset consisting of 107 makeup-transformations taken from random YouTube makeup video tutorials.Each subject is attempting to spoof a target identity. Hence we provide three sets of face images: images of a subject before makeup; images of the same subject after makeup with the intention of spoofing; and images of the target subject who is being spoofed

CIL:10898, Cavia porcellus, Sertoli cell

GitHub - BiDAlab/DiveFace: A new annotation database

Olivetti is a face images dataset that was made between 1992 and 1994 at AT&T Laboratories Cambridge. It contains 10 different images of 40 distinct people with 400 face images. Besides the fact that the images have the same background and same size, the images were converted to gray level and pixel values were scaled from 0 to 1 CoMA CoMA. License. Sign In. CoMA. Generating 3D faces using Convolutional Mesh Autoencoders. Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. European Conference on Computer Vision (ECCV) 2018, Munich, Germany. Abstract. Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking.

  1. About: FaceScape is a large-scale detailed 3D face dataset that includes 18,760 textured 3D face models, which are captured from 938 subjects and each with 20 specific expressions. Using the FaceScape dataset, the researchers studied how to predict a detailed face model from a single image. The dataset is released free for non-commercial research
  2. To solve this problem, we propose a semi-automatical way to collect face images from Internet and build a large scale dataset containing 10,575 subjects and 494,414 images, called CASIA-WebFace. To the best of our knowledge, the size of this dataset rank second in the literature, only smaller than the private dataset of Facebook (SCF)
  3. Face Mask Dataset (FMD) used in RetinaFaceMask is a collection of 7,959 images richly annotated with either face with a mask and face without a mask. This dataset does not contain the images for class face wearing mask incorrectly and mask area. MAskedFAces (MAFA) dataset is based on images collected from the internet. This dataset is a.
  4. However, there is a caveat you should be aware of when using this method to artificially create a dataset! If you use a set of images to create an artificial dataset of people wearing masks, you cannot re-use the images without masks in your training set — you still need to gather non-face mask images that were not used in the artificial generation process

Face Databases From Other Research Group

AR dataset contains sunglasses and scarf occlusion, which belongs to case 1 of occlusion FR. Extend Yela B dataset is a clean face dataset, which belongs to case 4. This part is used to verify the results on Webface. The performance of different attention modules on AR dataset and Extend Yela B dataset is listed in Table 2 Image data. Datasets consisting primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.. Facial recognition. In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces Face Mask Detection Application and Dataset. Face Mask Detection Application and Dataset Dr Daniel Matthias, Managwu Chidozie Abstract — The COVID-19 is an ongoing crisis that has resulted in a large number of casualties and security concerns. People also wear masks to cover themselves in order to reduce the spread of the coronavirus virus Law enforcement also has access to non-public face datasets, such as current mugshots and driver's license images. Some of the larger face databases are Labeled Faces in the Wild,.

FACE BY THANDI6208 EAST RED BRIDGE RD, KANSAS CITY, MO 64134. FACE BY THANDI Prepare the dataset and build a TextDataset. The next step is to extract the instructions from all recipes and build a TextDataset.The TextDataset is a custom implementation of the Pytroch Dataset class implemented by the transformers library. If you want to know more about Dataset in Pytorch you can check out this youtube video.. First, we split the recipes.json into a train and test section

10,044 non-face images Over 3.8 million manual annotations to dat e Protocols supporting face detection, 1 :1, 1:N, and clustering Improved geographic distribution Data licensed for redistribution Features non-frontal pose, heavy occlusion, and low resolution images IJB-B Overvie Face detection pipeline based on the sliding window detector of Dalal and Triggs 2005. Given a training data set and testing data set, the detection pipeline: The negative training examples are sampled from a database of 275 non-face images from Wu et al. and the SUN scene database. The examples are. MegaFace is a large-scale public face recognition training dataset that serves as one of the most important benchmarks for commercial face recognition vendors. It includes 4,753,320 faces of 672,057 identities from 3,311,471 photos downloaded from 48,383 Flickr users' photo albums. All photos included a Creative Commons licenses, but most were.

KomNET: Face Image Dataset from Various Media for Face

A major driver of bias in face recognition, as well as other AI tasks, is the training data. Deep face recognition networks are often trained on large-scale training datasets, such as CASIA-WebFace, VGGFace2 and MSCeleb-1M, which all contain racial bias. Thus, social awareness must be brought to the building of datasets for training These two scenarios represent a more non-cooperative subject and he is asked to be spontaneous. Three levels of zoom are captured in each video in order to cover a broad range of face resolutions. The dataset consists of: High-resolution 3D scans of human faces from many subjects FaceScape dataset provides 3D face models, parametric models and multi-view images in large-scale and high-quality. The camera parameters, the age and gender of the subjects are also included. The data have been released to public for non-commercial research purpose. -> Learn more about data. -> Apply to download.-> View tools on Github The MUCT Face Database The Yale Face Database B The Yale Face Database PIE Database The UMIST Face Database Olivetti - Att - ORL The Japanese Female Facial Expression (JAFFE) Database The Human Scan Database The University of Oulu Physics-Based Face Database XM2VTSDB Databases with over 100 unique individuals in the

The dataset containing Jillian York's face is one of a series compiled on behalf of Iarpa (earlier iterations are IJB-A and -B), which have been cited by academics in 21 different countries. The morphed face images in this data set are licensed under the Creative Commons Attribution-ShareAlike 3.0 License (CC BY-SA 3.0). You are free to share, to copy, distribute and transmit the data and to remix or adapt the data under the following conditions: The AMSL Face Morph Image Data Set includes A million faces for face recognition at scale. MegaFace is the largest publicly available facial recognition dataset. Toggle navigation. MegaFace. MegaFace Dataset. Researcher shall use the Database only for non-commercial research and educational purposes Synopsis. The Yale Face Database (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink Bottom Line - The dataset is available for research and non-commercial use. The LPW dataset is a robust dataset that offers both indoor and outdoor images and has ground-truth annotations that make it a very useful dataset for teams looking for high-variance datasets

2017. UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc Computer Vision and Pattern Recognition (CVPR), 2017. [ PDF] [bib] Semantic Understanding of Scenes through ADE20K Dataset. Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso and Antonio Torralba. International Journal on Computer Vision (IJCV). [PDF] [bib A dataset, or data set, is simply a collection of data. The simplest and most common format for datasets you'll find online is a spreadsheet or CSV format — a single file organized as a table of rows and columns. But some datasets will be stored in other formats, and they don't have to be just one file. Sometimes a dataset may be a zip. CMU Face Datasets - Testing images for the face detection task, and the facial expression database; Public Figures Face Database - The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects

Kernel tricks and nonlinear dimensionality reduction viai·bug - research - Face analysis

The Non-Treachery of Dataset

New Datasets for Disguised Face Recognition by Kate

IJB-C Dataset Papers With Cod

Tracking Chinese Investment: Western Hemisphere Now Top

lapse, which causes an increase in false non-match rates. Reporting summary performance measures for subsets of a facial aging dataset does not provide any insight into how the genuine similarity scores of individuals are changing over time. Previous facial aging studies [19] primarily used FG-NET [17] and MORPH [18] face datasets, which ar Three datasets were used as benchmarks to evaluate the proposed methodology. The SVM classifier achieved the highest accuracy with 99.64%. In (Ge, Li, Ye, & Luo, 2017). The authors proposed a model and dataset to find the normal and masked face in the wild. They introduced a large dataset Masked Faces (MAFA), which has 35, 806 masked faces Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the. Stanford Dogs Dataset Aditya Khosla Nityananda Jayadevaprakash Bangpeng Yao Li Fei-Fei. Stanford University. The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization face alignment datasets. AnimalWeb is bigger (in terms of faces offered) than 80% of the datasets targeted at human face align-ment. Further, the existing efforts on animal face datasets are lim-ited to only single species. This work targets a big gap in this area by building a large-scale annotated animal faces dataset The false detected non-face samples were used as new samples to train the classifier and the new trained classifier classified the testing dataset, which is repeated until the classifier meets the requirements. Sung and Poggio used this kind of bootstrap to get the non-face samples and trained neural network