CWNN-Net: A New Convolution Wavelet Neural Network for Gender Classification using Palm Print

The human hand is one of the body parts with special characteristics that are unique to every individual. The distinctive features can give some information about an individual, thus, making it a suitable body part that can be relied upon for biometric identification and, specifically, gender recognition. Several studies have suggested that the hand has unique traits that help in gender classification. Human hands form part of soft biometrics as they have distinctive features that can give information about a person. Nevertheless, the information retrieved from the soft biometrics can be used to identify an individual’s gender. Furthermore, the soft biometrics can be combined with the main biometrics characteristics that can improve the quality of biometric detection. Gender classification using hand features, such as palm contributes significantly to the biometric identification domain and, hence, presents itself as a valuable research topic. This study explores the use of Discrete Wavelet Transform (DWT) in gender identification, with SqueezeNet acting as a tool for unsheathing features, and Support Vector Machine (SVM) operating as discriminative classifier. Inference is made using mode voting approach. Notably, the two datasets that were crucial for the fulfillment of the study were the 11k database and CASIA. The outcome of the tests substantiated the use of voting technique for gender recognition.

@article{Elgallad2019,
  title     = {CWNN-Net: A New Convolution Wavelet Neural Network for Gender Classification using Palm Print},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {5},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Elaraby A Elgallad and Wael Ouarda and Adel M. Alimi},
  doi       = {10.14569/IJACSA.2019.0100516},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100516}
}