Landmark Free Face Attribute Prediction.

Face attribute prediction in the wild is important for many facial analysis applications yet it is very challenging due to ubiquitous face variations. In this paper, we address face attribute prediction in the wild by proposing a novel method, lAndmark Free Face AttrIbute pRediction (AFFAIR). Unlike traditional face attribute prediction methods that require facial landmark detection and face alignment, AFFAIR uses an endto- end learning pipeline to jointly learn a hierarchy of spatial transformations that optimize facial attribute prediction with no reliance on landmark annotations or pre-trained landmark detectors. AFFAIR achieves this through simultaneously 1) learning a global transformation which effectively alleviates negative effect of global face variation for the following attribute prediction tailored for each face, 2) locating the most relevant facial part for attribute prediction and 3) aggregating the global and local features for robust attribute prediction. Within AFFAIR, a new competitive learning strategy is developed that effectively enhances global transformation learning for better attribute prediction. We show that with zero information about landmarks, AFFAIR achieves state-of-the-art performance on three face attribute prediction benchmarks, which simultaneously learns the face-level transformation and attribute-level localization within a unified framework.

Discriminant subspace analysis: a Fukunaga-Koontz approach.

The Fisher Linear Discriminant (FLD) is commonly used in pattern recognition. It finds a linear subspace that maximally separates class patterns according to the Fisher Criterion. Several methods of computing the FLD have been proposed in the literature, most of which require the calculation of the so-called scatter matrices. In this paper, we bring a fresh perspective to FLD via the Fukunaga-Koontz Transform (FKT). We do this by decomposing the whole data space into four subspaces with different discriminability, as measured by eigenvalue ratios. By connecting the eigenvalue ratio with the generalized eigenvalue, we show where the Fisher Criterion is maximally satisfied. We prove the relationship between FLD and FKT analytically, and propose a unified framework to understanding some existing work. Furthermore, we extend our our theory to Multiple Discriminant Analysis (MDA). This is done by transforming the data into intra- and extra-class spaces, followed by maximizing the Bhattacharyya distance. Based on our FKT analysis, we identify the discriminant subspaces of MDA/FKT, and propose an efficient algorithm, which works even when the scatter matrices are singular, or too large to be formed. Our method is general and may be applied to different pattern recognition problems. We validate our method by experimenting on synthetic and real data.

Continuous verification using multimodal biometrics.

Conventional verification systems, such as those controlling access to a secure room, do not usually require the user to reauthenticate himself for continued access to the protected resource. This may not be sufficient for high-security environments in which the protected resource needs to be continuously monitored for unauthorized use. In such cases, continuous verification is needed. In this paper, we present the theory, architecture, implementation, and performance of a multimodal biometrics verification system that continuously verifies the presence of a logged-in user. Two modalities are currently used--face and fingerprint--but our theory can be readily extended to include more modalities. We show that continuous verification imposes additional requirements on multimodal fusion when compared to conventional verification systems. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for continuous verification and propose new metrics against which we benchmark our system.