Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, this paper proposes a semi-automatical way to collect face images from Internet and builds a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on the database, we use a 11-layer CNN to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. The publication of CASIAWebFace will attract more research groups entering this field and accelerate the development of face recognition in the wild. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of distractors (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a person's age, and compare as a function of training data size (number of photos and people). We report results of state of the art and baseline algorithms. Our key observations are that testing at the million scale reveals big performance differences (of algorithms that perform similarly well on smaller scale) and that age invariant recognition as well as pose are still challenging for most. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations at: megaface.cs.washington.edu. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> Although considerable work has been done in recent years to drive the state of the art in facial recognition towards operation on fully unconstrained imagery, research has always been restricted by a lack of datasets in the public domain. In addition, traditional biometrics experiments such as single image verification and closed set recognition do not adequately evaluate the ways in which unconstrained face recognition systems are used in practice. The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases. IJB-C adds 1,661 new subjects to the 1,870 subjects released in IJB-B, with increased emphasis on occlusion and diversity of subject occupation and geographic origin with the goal of improving representation of the global population. Annotations on IJB-C imagery have been expanded to allow for further covariate analysis, including a spatial occlusion grid to standardize analysis of occlusion. Due to these enhancements, the IJB-C dataset is significantly more challenging than other datasets in the public domain and will advance the state of the art in unconstrained face recognition. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Face Datasets <s> Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition. <s> BIB006
Due to the wide application of face recognition in the industry, a large number of datasets are proposed for that purpose. We will introduce some of the most popular ones here. Yale and Yale Face Database B: Yale face dataset is perhaps one of the earliest face recognition datasets  . It Contains 165 grayscale images 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). It is extended version, Yale Face Database B [32], contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). For every subject in a particular pose, an image with ambient (background) illumination was also captured. Ten example images from Yale face B dataset are shown in Figure 9 . CMU Multi-PIE: The CMU Multi-PIE face database contains more than 750,000 images of 337 people [83],  . Subjects were imaged under 15 view points and 19 illumination conditions while displaying a range of facial expressions. Labeled Face in The Wild (LFW): Labeled Faces in the Wild is a database of face images designed for studying unconstrained face recognition. The database contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured [85] . 1680 of the people pictured have two or more distinct photos in the database. The only constraint on these faces is that they were detected by the Viola-Jones face detector. For more details on this dataset, we refer the readers to the database web-page. PolyU NIR Face Database: The Biometric Research Centre at The Hong Kong Polytechnic University developed a NIR face capture device and used it to construct a large-scale NIR face database [86] . By using the self-designed data acquisition device, they collected NIR face images from 335 subjects. In each recording, 100 images from each subject is captured, and in total about 34,000 images were collected in the PolyU-NIRFD database. YouTube Faces: This data set contains 3,425 videos of 1,595 different people. All videos were downloaded from YouTube. An average of 2.15 videos are available for each subject. The goal of this dataset was to produce a large scale collection of videos along with labels indicating the identities of a person appearing in each video [87] . In addition, they published benchmark tests, intended to measure the performance of video pair-matching techniques on these videos. VGGFace2: VGGFace2 is a large-scale face recognition dataset  . Images are downloaded from Google Image Search and have a large variations in pose, age, illumination, ethnicity and profession. It contains 3.31 million images of 9131 subjects (identities), with an average of 362.6 images for each subject. Face distribution for different identities is varied, from 87 to 843. CASIA-WebFace: CASIA WebFace Facial dataset of 453,453 images over 10,575 identities after face detection BIB001 . This is one of the largest publicly available face datasets. MS-Celeb: Microsoft Celeb is a dataset of 10 million face images harvested from the Internet for the purpose of developing face recognition technologies, from nearly 100,000 individuals BIB003 . CelebA: CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images BIB002 . CelebA has a large diversity, large quantities, and rich annotations, including more than 10,000 identities, more than 202,599 face images, 5 landmark locations, 40 binary attributes annotations per image. The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, landmark (or facial part) localization, and face editing & synthesis. IJB-C: The IJB-C dataset BIB005 contains about 3,500 identities with a total of 31,334 still facial images and 117,542 unconstrained video frames. The entire IJB-C testing protocols are designed to test detection, identification, verification and clustering of faces. In the 1:1 verification protocol, there are 19,557 positive matches and 15,638,932 negative matches. MegaFace: MegaFace Challenge BIB004 is a publicly available benchmark, which is widely used to test the performance of facial recognition algorithms (for both identification and verification). The gallery set of MegaFace contains over 1 million images from 690K identities collected from Flickr  . The probe sets are two existing databases: FaceScrub and FGNet. The FaceScrub dataset contains 106,863 face images of 530 celebrities. The FGNet dataset is mainly used for testing age invariant face recognition, with 1002 face images from 82 persons. Other Datasets: It is worth mentioning that there are several other datasets which we skipped the details due to being private or less popularity, such as Deep-Face (Facebook private dataset of 4.4M photos of 4k subjects), NTechLab (a private dataset of 18.4M photos of 200k subjects), FaceNet (Google private dataset of more than 500M photos of more than 10M subjects), WebFaces (a dataset of 80M photos crawled from web) BIB004 , and Disguised Faces in the Wild (DFW) BIB006 which contains over 11,000 images of 1,000 identities with variations across different types of disguise accessories.