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The recently proposed Facial Trait Code (FTC) formulates the component-based face recognition problem as a coding problem using Error-Correcting Code. The development of FTC is based on the extraction of local feature patterns from a large set of faces without significant variations in expression and illumination. This paper reports a new type of FTC that encompasses the faces with large expression variation and under various illumination conditions. We assume that if the patches of a local feature on two different faces look similar in appearance, this pair of patches will also show similar visual patterns when both faces change expressions and are under different illumination conditions. With this assumption, we propose the Polymorphous Facial Trait Code for face recognition under illumination and expression variations. The proposed method outperforms the original Facial Trait Code substantially in solving a strict face verification problem, in which only one facial image per individual is available for enrolling to the gallery set, and the probe set consists of facial images with strong illumination and expression variations. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. The mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the ˵Mobile Biometry″ (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems. The results have shown that the best performing face and speaker verification systems obtained the same level of performance, respectively 10.9% and 10.6% of HTER. [ABSTRACT FROM AUTHOR]

Recently, Facial Trait Code (FTC) was proposed for solving face recognition, and was reported with promising recognition rates. However, several simplifications in the FTC encoding make it unable to handle the most rigorous face recognition scenario in which only one facial image per individual is available for enrollment in the gallery set and the probe set includes faces under variations caused by illumination, expression, pose or misalignment. In this study, we propose the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. We also proposed the Pattern-Specific Subspace Learning (PSSL) scheme that encodes and recognizes faces robustly under aforementioned variations. The proposed PFTC was evaluated and compared with state-of-the-art algorithms, including the FTC, the algorithm using sparse representation, and the one using Local Binary Pattern. Our experimental study considered factors such as the number of enrollment allowed in the gallery, the variation among gallery or probe set, and reported results for both identification and verification problems. The proposed PFTC yielded significant better recognition rates in most of the scenarios than all the states-of-the-art algorithms evaluated in this study. [ABSTRACT FROM AUTHOR]

This paper proposes a novel computation method for tool path planning in 5-axis flank milling of ruled surfaces. This method converts the path planning (a geometry problem) into a curve matching task (a mathematical programming problem). Discrete dynamic programming techniques are applied to obtain the optimal matching with machining error as the objective function. Each matching line corresponds to a cutter location and the tool orientation at the location. An approximating method based on z-buffer is developed for a quick estimation of the error. A set of parameters is allowed to vary in the optimization, thus generating the optimal tool paths in different conditions. They reveal useful insights into design of the tool motion pattern with respect to the surface geometry. The simulation result of machining different surfaces validates the proposed method. This work provides an effective systematic approach to precise error control in 5-axis flank milling. [ABSTRACT FROM AUTHOR]

We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of the basic types of local facial features, called facial trait bases, which can be extracted from a large number of faces. In addition, the fusion of these facial trait bases can accurately capture the appearance of a face. Extraction of the facial trait bases involves clustering and boosting approaches, leading to the best discrimination of the human faces. The extracted facial trait bases are symbolized and make up the n-ary facial trait codes. A given face can be then encoded at the patches specified by the traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest trait base. We applied FTC to a typical face identification problem, and it yielded satisfactory results under different illumination conditions. [ABSTRACT FROM AUTHOR]

Mixed emotions, especially those in conflict, sway agent decisions and result in dramatic changes in social scenarios. However, the emotion models and architectures for virtual agents are not yet advanced enough to be imbued with coexisting emotions. In this paper, an improved emotion model integrated with decision making algorithms is proposed to deal with two topics: the generation of coexisting emotions, and the resolution to ambivalence, in which two emotions conflict. A scenario of ambivalence is provided to illustrate the process of agent's decision-making. [ABSTRACT FROM AUTHOR]