Videos have ample amount of information in the form of frames that can be utilized for feature extraction and matching. However, face images in not all of the frames are 'memorable'. Utilizing all the frames available in a video for recognition does not necessarily improve the performance but significantly increases the computation time. In this research, we present a memorability based frame selection algorithm that enables automatic selection of memorable frames for facial feature extraction and matching. A deep learning based face recognition algorithm is then proposed that utilizes a stack of denoising autoencoders and deep Boltzmann machines to perform face recognition using the most memorable frames. The proposed algorithm, termed as MDLFace, is evaluated on two publicly available video face databases, YouTube Faces and Point and Shoot Challenge. The results show that the proposed algorithm achieves state-of-the-art performance even at low false accept rates.
Face recognition algorithms generally use 2D images for feature extraction and matching. In order to achieve better performance, 3D faces captured via specialized acquisition methods have been used to develop improved algorithms. While such 3D images remain dif?cult to obtain due to several issues such as cost and accessibility, RGB-D images captured by low cost sensors (e.g. Kinect) are comparatively easier to acquire. This research introduces a novel face recognition algorithm for RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. The probe RGB-D descriptor is used as input to a random decision forest classi?er to establish the identity. This research also presents a novel RGB-D face database pertaining to 106 individuals. 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.
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.
the CAPTCHA, users must correctly identify visually-distorted human faces embedded in a complex background without selecting any non-human faces. The proposed algorithm generates a CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches.