Abstract
This paper devises a filter bank approach to extract the local structure knowledge of a face image by computing its probability distribution function from the filter responses. Independent Component Analysis (ICA) filters are embraced in this work. Considering the limitation of ICA filter learning in handling the image conditions with uncontrolled facial expressions, illuminations, aging, etc., we proposed an independent statistical descriptor, coined ISD, in this paper with the aim to handle the rampant images. ISD intensifies ICA response invariance through hashing the filter responses by encoding the relation between each response element and its neighbour and then block-wise histogramming the output. In addition, an overlapping average pooling is executed to regulate the histogram features, prior to whitening PCA compression. The good performance of ISD descriptors has been extensively corroborated in the empirical results on face recognition.
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Authors would like to thanks Ministry of Higher Education (Malaysia) FRGS (#MMUE/140020) for the financial support.
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Han, P.Y., Ling, G.F., Yin, O.S. (2017). Independent Statistical Descriptor in Face Recognition. In: Duy, V., Dao, T., Kim, S., Tien, N., Zelinka, I. (eds) AETA 2016: Recent Advances in Electrical Engineering and Related Sciences. AETA 2016. Lecture Notes in Electrical Engineering, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-50904-4_27
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