Abstract
Gender identification is a significant task which is very useful in many computer applications like human–computer interaction, surveillance, demographic studies, and forensic studies. Being one of the most popular soft biometrics, gender information plays a vital role in improvement of the accuracy of biometric systems. In this paper, we have presented an approach based on multiresolution statistical descriptors derived from histogram of Discrete Wavelet Transform. First, the input facial image was enhanced by applying contrast limited adaptive histogram equalization. During feature extraction, multiresolution statistical descriptors were computed and fed into the Nearest Neighbor, Support Vector Machine, and Linear Discriminant Analysis classifiers respectively. We have achieved encouraging accuracy for gender identification on complex dataset of frontal facial images.
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Acknowledgements
We are thankful to Smt. Savitri A. Nawade for participation in the creation of database for the experimentation work stated in this paper.
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Prabha, Sheetlani, J., Dhawale, C., Pardeshi, R. (2019). Gender Identification from Frontal Facial Images Using Multiresolution Statistical Descriptors. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_99
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DOI: https://doi.org/10.1007/978-981-13-1513-8_99
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