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On RGB-D face recognition using Kinect

The experimental results indicate that the RGB-D information obtained by Kinect can be used to achieve improved face recognition performance compared to existing 2D and 3D approaches.
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Recognizing composite sketches with digital face images via SSD dictionary

The proposed algorithm utilizes a SSD based dictionary generated via 50,000 images from the CMU Multi-PIE database, and the gallery-probe feature vectors created using SSD dictionary are matched using GentleBoostKO classifier.
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RGB-D Face Recognition With Texture and Attribute Features

The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared with existing 2D and 3D approaches.
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FaceDCAPTCHA: Face detection based color image CAPTCHA

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FR-CAPTCHA: CAPTCHA Based on Recognizing Human Faces

This work proposes FR-CAPTCHA, a Turing test based on finding matching pairs of human faces in an image which achieves a human accuracy of 94% and is robust against automated attacks.
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Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition

This paper attempts to unravel three aspects related to the robustness of DNNs for face recognition in terms of vulnerabilities to attacks, detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and making corrections to the processing pipeline to alleviate the problem.
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Face Verification via Learned Representation on Feature-Rich Video Frames

Experimental analysis suggests that the proposed feature-richness-based frame selection offers noticeable and consistent performance improvement compared with frontal only frames, random frames, or frame selection using perceptual no-reference image quality measures and joint feature learning in SDAE and sparse and low rank regularization in DBM helps in improving face verification performance.
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MDLFace: Memorability augmented deep learning for video face recognition

A memorability based frame selection algorithm is presented that enables automatic selection of memorable frames for facial feature extraction and matching and achieves state-of-the-art performance at low false accept rates.
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Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks

This paper attempts to unravel three aspects related to the robustness of DNNs for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world, and presents several effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustnessof DNN-based face recognition.
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Group sparse representation based classification for multi-feature multimodal biometrics

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