Lightweight Face Recognition

The LFW dataset contains 13,233 images of faces collected from the web. This dataset consists of the 5749 identities with 1680 people with two or more images. In the standard LFW evaluation protocol the verification accuracies are reported on 6000 face pairs. Source: A Performance Evaluation of Loss Functions for Deep Face Recognition Image Source: http://vis-www.cs.umass.edu/lfw

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 Image Source: https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf

The IJB-B dataset is a template-based face dataset that contains 1845 subjects with 11,754 images, 55,025 frames and 7,011 videos where a template consists of a varying number of still images and video frames from different sources. These images and videos are collected from the Internet and are totally unconstrained, with large variations in pose, illumination, image quality etc. In addition, the dataset comes with protocols for 1-to-1 template-based face verification, 1-to-N template-based open-set face identification, and 1-to-N open-set video face identification. Source: An Automatic System for Unconstrained Video-Based Face Recognition Image Source: https://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. Source: CALFW

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. There are three motivations behind the construction of CPLFW benchmark as follows: 1.Establishing a relatively more difficult database to evaluate the performance of real world face verification so the effectiveness of several face verification methods can be fully justified. 2.Continuing the intensive research on LFW with more realistic consideration on pose intra-class variation and fostering the research on cross-pose face verification in unconstrained situation. The challenge of CPLFW emphasizes pose difference to further enlarge intra-class variance. Also, negative pairs are deliberately selected to avoid different gender or race. CPLFW considers both the large intra-class variance and the tiny inter-class variance simultaneously. 3.Maintaining the data size, the face verification protocol which provides a 'same/different' benchmark and the same identities in LFW, so one can easily apply CPLFW to evaluate the performance of face verification. Source: CPLFW

Large, multimodal biometric dataset: It contains still images and videos of over 1,000 people captured at various ranges (up to 1,000 meters) and elevations (up to 400 meters) using a diverse set of cameras (commercial, military-grade, specialized). Focus on challenging scenarios: Designed to address limitations of existing datasets, it includes data with extreme poses, low resolutions, and atmospheric distortions to push the boundaries of biometric recognition algorithms. Rich annotations and diversity: Each data point is accompanied by detailed metadata (sensor details, weather conditions) and manual annotations (bounding boxes, yaw/pitch angles) for face and whole-body regions. The dataset also ensures demographic diversity for robust algorithm development.