Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an e...

This paper examines application of various feature domains for recognition of human face images to introduce an efficient feature extraction method. The proposed feature extraction method comprised of two steps. In the first step, a human face localization technique with defining a new parameter to eliminate the effect of irrelevant data is applied to the facial images. In the next step three d...

– A new approach to feature based frontal face recognition with Gabor wavelets is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. There is no training as in neural network approaches, thus single frontal face for each individual is enough as reference. Experiment...

In this paper, we improve an object detection approach using spatial histogram features, by applying classifier ensemble. The spatial histogram features can preserve texture and shape information of an object, simultaneously. We train a hierarchical classifier by combining cascade histogram matching and the combination of Multi Layer Perceptrons. The cascade histogram matching is trained via au...

This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are learnt from the training set instead of arbitrarily defined. The learning procedure consists of two steps. First, a modified version of NMF (Non-negative Matrix Factorization), namely local NMF (LNMF), is applied to ge...

The principal component analysis (PCA) faces the problem of high computation complexity and inaccurate estimated covariance matrix from training face images for face recognition. The expressive feature face recognition algorithm (EFFRA) is proposed. In EFFRA, the subspace basic vector extracted by PCA is substituted by the right singular vectors of training images, so that the transformation fr...

Accurate biometric system for authentication is the need of the hour in today’s scenario. In face spoofing attack a person tries to pretend to be a valid user by using photo or video of an authorized person and gets illegitimate access. Hence it is essential to develop a robust and authentic face spoof detection system in order to protect the privacy about the person. Centre of attraction of th...

This paper proposes a new approach for face verification, where a pair of images needs to be classified as belonging to the same person or not. This problem is relatively new and not well-explored in the literature. Current methods mostly adopt techniques borrowed from face recognition, and process each of the images in the pair independently, which is counter intuitive. In contrast, we propose...

The combining classifier approach has proved to be a proper way for improving recognition performance in the last two decades. This paper proposes to combine local and global facial features for face recognition. In particular, this paper addresses three issues in combining classifiers, namely, the normalization of the classifier output, selection of classifier(s) for recognition, and the weigh...

We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab code), which is mainly inspired by spatial pyramid pooling in generic image classification. We show that features formed by simply pooling local patches over a multi-level pyramid, coupled with a linear classifier, can significantly outperform most recent face ...