We investigate 3D face recognition by proposing an algorithm with the following processing stages: (a) thresholding of depth maps of 3D range images; (b) normalization and alignment; c) feature extraction by Gabor Wavelet Filtering (GWF); d) Principal Component Analysis (PCA); e) classification using the concurrent neural model previously proposed by the first author called Concurrent Self-Orga...

In this paper, a novel 3D face recognition scheme by Sliding Complex Wavelet Structural SIMilarity (SCW-SSIM) index carried out on detailed geometry images is proposed. First, the preprocessed 3D faces are mapped into geometry images on 2D plane. Then, a multi-scale wavelet transform is designed to decompose geometry image into a set of sub-images and denotes high-pass sub-images of wavelet tra...

Morphable face models have proven to be an effective tool for 3D face modeling and face recognition, but the extension to 3D face scans with expressions is still a challenge. The two main difficulties are (1) how to build a new morphable face model that deals with expressions, and (2) how to fit this morphable face model automatically to new 3D face scans with unknown expressions. This work pre...

Realistic 3D face model is more precise in representing pose, illumination, and expression of face than 2D face model so that it can be utilized usefully in various applications such as face recognition, games, avatars, animations, and etc. In this paper, we propose a 3D face modeling method based on 3D dense morphable shape model. The proposed 3D modeling method first constructs a 3D dense mor...

Face recognition is one of the most important and rapidly advancing areas of computer science. Increased recent interest in improving commercial security systems has lead to intensive research into biometric identification and verification applications. Whilst a number of biometrics are potentially available for human recognition the face can usually be captured with the greatest degree of " pa...

An often reason for face recognition system failure is image changing because of head rotation. The way for improving face recognition is some kind of image transformation, for example, to some given head position. Unfortunately image transformation itself (without using information about spatial geometry of a face) in most cases does not provide adequate synthetic photograph and results in low...

Face recognition is one of the most important and rapidly advancing areas of computer science. Increased recent interest in improving commercial security systems has lead to intensive research into biometric identification and verification applications. Whilst a number of biometrics are potentially available for human recognition the face can usually be captured with the greatest degree of “pas...

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions that can distinguish betw...

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the ...

A new approach, called Collective Shape Difference Classifier (CSDC), is proposed to improve the accuracy and computational efficiency of 3D face recognition. The CSDC learns the most discriminative local areas from the Pure Shape Difference Map (PSDM) and trains them as weak classifiers for assembling a collective strong classifier using the real-boosting approach. The PSDM is established betw...