A new look at filtering techniques for illumination invariance in automatic face recognition

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals.

A pose-wise linear illumination manifold model for face recognition using video

The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illumination manifold-based normalization; (ii) pose robustness is achieved by decomposing each appearance manifold into semantic Gaussian pose clusters, comparing the corresponding clusters and fusing the results using an RBF network; (iii) a fully automatic recognition system based on the proposed method is described and extensively evaluated on 600 head motion video sequences with extreme illumination, pose and motion pattern variation. On this challenging data set our system consistently demonstrated a very high recognition rate (95% on average), significantly outperforming state-of-the-art methods from the literature.

An information-theoretic approach to face recognition from face motion manifolds

In this work, we consider face recognition from face motion manifolds (FMMs). The use of the resistor-average distance (RAD) as a dissimilarity measure between densities confined to FMMs is motivated in the proposed information-theoretic approach to modelling face appearance. We introduce a kernel-based algorithm that makes use of the simplicity of the closed-form expression for RAD between two Gaussian densities, while allowing for modelling of complex and nonlinear, but intrinsically low-dimensional manifolds. Additionally, it is shown how geodesically local FMM structure can be modelled, naturally leading to a stochastic algorithm for generalizing to unseen modes of data variation. Recognition performance of our method is demonstrated experimentally and is shown to exceed that of state-of-the-art algorithms. Recognition rate of 98% was achieved on a database of 100 people under varying illumination

An illumination invariant face recognition system for access control using video

Illumination and pose invariance are the most challenging aspects of face recognition. In this paper we describe a fully automatic face recognition system that uses video information to achieve illumination and pose robustness. In the proposed method, highly nonlinear manifolds of face motion are approximated using three Gaussian pose clusters. Pose robustness is achieved by comparing the corresponding pose clusters and probabilistically combining the results to derive a measure of similarity between two manifolds. Illumination is normalized on a per-pose basis. Region-based gamma intensity correction is used to correct for coarse illumination changes, while further refinement is achieved by combining a learnt linear manifold of illumination variation with constraints on face pattern distribution, derived from video. Comparative experimental evaluation is presented and the proposed method is shown to greatly outperform state-of-the-art algorithms. Consistent recognition rates of 94-100% are achieved across dramatic changes in illumination.

The janus face of new media propaganda: The case of Patani neojihadist YouTube warfare and its Islamophobic effect on cyber-actors

Surfing on the Internet 2.0 revolution, Patani 2.0 has allowed Patani neojihadist militants to access new competitive spaces and create their own imagined online community by penetrating new realms of the Internet. This article discusses the use of new media militant propaganda by Patani militants and how it is Janus faced. It further examines how the Patani 2.0 social interaction enabled by social media such as YouTube leads to group cohesion among certain actors and the formation of a collective identity that is clustered around the notions of Muslim victimization and defensive jihad; and how, at the same time, it reinforces antithetical identities and fosters group identity competition, where one religious group is often pitted against another. As a result, the Janus effect of Patani neojihadist YouTube online propaganda, while it primarily seeks to radicalize, also generates a reactionary, often virulent, anti-Muslim response from the movement's critics.

Do cyclic changes in women's face preferences target cues to long-term health?

A shift to prefer more masculine male faces when conception risk is high may be an adaptation for finding mates with good disease resistance. We investigated whether preferences for other facial cues to long-term health also increase when conception is likely. We examined preferences for the faces of men with good health histories and for facial averageness and symmetry, two putative indicators of health during development. Preferences were tested at two points in the menstrual cycle that differed maximally in conception risk. No cyclic changes in preferences were found. We consider implications for the sexual selection of variation in preferences across the menstrual cycle.