Abstract-In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the Linear Discriminant Analysis (LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, we propose a new face recognition method called the Discriminative Common Vector method based on a variation of Fisher's Linear Discriminant Analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's Linear Discriminant criterion given in the paper. Our test results show that the Discriminative Common Vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.
Acoustic properties of speech have previously been identified as possible cues to depression, and there is evidence that certain vocal parameters may be used further to objectively discriminate between depressed and suicidal speech. Studies were performed to analyze and compare the speech acoustics of separate male and female samples comprised of normal individuals and individuals carrying diagnoses of depression and high-risk, near-term suicidality. The female sample consisted of ten control subjects, 17 dysthymic patients, and 21 major depressed patients. The male sample contained 24 control subjects, 21 major depressed patients, and 22 high-risk suicidal patients. Acoustic analyses of voice fundamental frequency (Fo), amplitude modulation (AM), formants, and power distribution were performed on speech samples extracted from audio recordings collected from the sample members. Multivariate feature and discriminant analyses were performed on feature vectors representing the members of the control and disordered classes. Features derived from the formant and power spectral density measurements were found to be the best discriminators of class membership in both the male and female studies. AM features emerged as strong class discriminators of the male classes. Features describing Fo were generally ineffective discriminators in both studies. The results support theories that identify psychomotor disturbances as central elements in depression and suicidality.
In some pattern recognition tasks, the dimension of the sample space is larger than the number of samples in the training set. This is known as the "small sample size problem". Linear discriminant analysis (LDA) techniques cannot be applied directly to the small sample size case. The small sample size problem is also encountered when kernel approaches are used for recognition. In this paper, we attempt to answer the question of "How should one choose the optimal projection vectors for feature extraction in the small sample size case?" Based on our findings, we propose a new method called the kernel discriminative common vector method. In this method, we first nonlinearly map the original input space to an implicit higher dimensional feature space, in which the data are hoped to be linearly separable. Then, the optimal projection vectors are computed in this transformed space. The proposed method yields an optimal solution for maximizing a modified Fisher's linear discriminant criterion, discussed in the paper. Thus, under certain conditions, a 100% recognition rate is guaranteed for the training set samples. Experiments on test data also show that, in many situations, the generalization performance of the proposed method compares favorably with other kernel approaches.
There is much interest in developing methods that enable humans and robots to interact in a natural and unencumbered fashion. In natural human-robot interactions, the human can be considered as a passive user since he/she does not have to behave in an artificial manner. The robot is in the direct physical presence of the human, who does not need to use any unnatural interfaces such as the keyboard or mouse to interact with the robot. In this research, a taxonomy is introduced to cover important considerations for human-robot interactions. As an application of passive human-robot interaction, two modalities for localizing humans based on sound source localization and infrared motion detection have been developed and integrated with the face-tracker system of a humanoid, Intelligent Soft Arm Control (ISAC), in order to direct ISACs attention and to prevent it from being quickly distracted. The sound source localization and passive infrared (PIR) motion detection systems are used to provide the face-tracker system with candidate regions for finding a face. In order to avoid the situation where the robot appears to be "hyperactive" and cannot give sufficient attention to a newly discovered face, these sensing modules should not directly gain control of the tracking if the system has recently acquired a new face. Our goal is to allow a human to redirect the attention of the system but give the system a method to ignore the distraction if recently engaged.
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