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Abstract: It has been found that facial thermograms vary with ambient temperature, as well as other internal and external conditions, and result in severe decline in the facial recognition rate. To tackle this problem, a skin heat transfer (SHT) model based on thermal physiology is derived in this paper. The proposed model converts the facial thermograms into blood-perfusion data, which is revealed to reduce the within-class scatter of face images. The advantage of the derived blood-perfusion data over the raw thermograms for recognition is analyzed by the normalized reverse cumulative histogram. It is shown that blood-perfusion data are more consistent in representing facial features. The experiments conducted on both same-session and time-lapse data have further demonstrated that (1) the blood-perfusion data are less sensitive to ambient temperature, physiological and psychological conditions if the human bodies are in the steady state; (2) for time-lapse data, the performance with the blood-perfusion data is nearly identical to that of the same-session data, while the recognition rate with the temperature data dramatically decreases in this case. The major contributions of this work are the well-grounded infrared data preprocessing and the corresponding face recognition system. [Copyright &y& Elsevier]

Abstract: In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement. For images compressed using the DCT, e.g., in JPEG or MPEG standard, the PCA and LDA can be directly implemented in the DCT domain such that the inverse DCT transform can be skipped and the dimensionality of the original data can be initially reduced to cut down computational cost. [Copyright &y& Elsevier]