Palmprint gender classification can revolutionise the performance of authentication systems, reduce searching space and speed up matching rate. However, to the best of their knowledge, there is no literature addressing this issue. The authors design a new convolutional neural network (CNN) structure, fine-tuning Visual Geometry Group Network, up to 19 layers to achieve a 20-layer network, for palmprint gender classification. Experimental results show that the proposed structure could achieve good performance for gender classification. They also investigate palmprint images with 15 different kinds of spectra. They empirically find that a palmprint image acquired by the Blue spectrum could achieve 89.2% correct classification and could be considered as a suitable spectrum for gender classification. The neural network is able to classify a 224 × 224 × 3-pixel palmprint image in <23 ms, verifying that the proposed CNN is an effective real-time solution.

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Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires Gaussian assumption, however, natural images are highly non-Gaussian so that GMM cannot be applied to perform image clustering task on pixel space. To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables with only the constraint of L-Lipschitz continuity. We call this model GAN-EM, which is a framework for image clustering, semi-supervised classification and dimensionality reduction. In M-step, we design a novel loss function for discriminator of GAN to perform maximum likelihood estimation (MLE) on data with soft class label assignments. Specifically, a conditional generator captures data distribution for K classes, and a discriminator tells whether a sample is real or fake for each class. Since our model is unsupervised, the class label of real data is regarded as latent variable, which is estimated by an additional network (E-net) in E-step. The proposed GAN-EM achieves state-of-the-art clustering and semi-supervised classification results on MNIST, SVHN and CelebA, as well as comparable quality of generated images to other recently developed generative models.

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