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Gender is one of the most useful facial attributes which are detected from human face images. In this work, we introduce a new gender classification system based on features extracted by Local Phase Quantization (LPQ) operators from intensity and Monogenic images. More detailed, the LPQ features are obtained from the input image (the intensity one) and from three other Monogenic components in the...
One of the most critical tasks in building a gender classification is how to describe the human face as a highly discriminative feature vector. To this end, in this paper we introduce a new handcrafted feature extraction method for unconstrained gender classification problem. From one input face image, we generate its smaller version and apply two LPQ operators on both of them. We then combine the...
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