Title :
Fast gender recognition by using a shared-integral-image approach
Author :
Shen, Bau-Cheng ; Chen, Chu-Song ; Hsu, Hui-Huang
Author_Institution :
Inst. of Inf. Sci., Acad. Sinica, Taipei
Abstract :
We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans´ gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results.
Keywords :
face recognition; feature extraction; gender issues; image classification; image coding; image representation; learning (artificial intelligence); support vector machines; AdaBoost learning; gender recognition; human face recognition; image classification; intensity variation encoding; nonlinear support vector machine; rectangle feature vector; shared-integral-image approach; Computer science; Detectors; Face detection; Face recognition; Humans; Image recognition; Information science; Neural networks; Support vector machine classification; Support vector machines; AdaBoost; Gender Recognition; Integral Image; Real AdaBoost; Support Vector Machine;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4959635
