International Journal of Biometrics (4 papers in press)
Human Age Classification using Appearance Features and Artificial Neural Network
by Jayant Jagtap, Manesh Kokare
Abstract: This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and Artificial Neural Network (ANN) as age classifier. The appearance features consists of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local Binary Pattern Histogram (LBPH) of regions of interest in face image are used as textural features. The ANN is designed by using two layer feedforward back propagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female respectively.
Keywords: Age classification; Appearance features; Artificial neural networkrn(ANN); Local Binary Pattern Histogram (LBPH).
Undecimated Discrete Wavelet Transform for Touchless 2D Fingerprint Identification
by Salah Ahmed Saeed Othman, Tarik Boudghene Stambouli
Abstract: Several recent research efforts in biometrics have focused on developing the touchless fingerprint identification system. Most of them using imaging resulting from cameras and mobile devices. The acquired images are firstly subjected to robust preprocessing steps to localize region of interest in order to extract its features. In the literature, touchless fingerprint features are generally based on algorithms designed for minutiae analysis in touch-based images. Because of perspective distortions and deformations in the samples, minutiae-based techniques can obtain poor results. This paper investigates multi-resolution decomposition features to overcome the limitations of using traditional minutiae algorithms in term of accuracy and matching speed. These decompositions are implemented on Hong Kong polytechnic university 2D touchless fingerprint database that contains 10080 images. Experimental results illustrate successful use of Undecimated Discrete Wavelet Transform (UDWT) and Discrete Wavelet Packet Transform (DWPT) which give better performance than Discrete Wavelet Transform (DWT) and minutiae based method with less calculation cost.
Keywords: biometrics; touchless fingerprint identification; fingerprint features extraction; wavelet transform; multi-resolution decomposition; minutiae feature.
Application of geometry to RGB images for facial landmark localization A preliminary approach
by Federica Marcolin, Enrico Vezzetti, Pietro Maroso
Abstract: This study proposes a novel approach to automatically localize 11 landmarks from facial RGB images. The novelty of this method relies on the application, i.e. point-by-point mapping, of 11 Differential Geometry descriptors such as curvatures to the three individual RGB image components. Thus, three-dimensional features are applied to bidimensional facial image representations and used, via thresholding techniques, to extract the landmark positions. The method was tested on the Bosphorus database and showed global average errors lower than 5 millimetres.
The idea behind this approach is to embed this methodology in state-of-the-art 3D landmark detection methods to accomplish a full automatic landmarking by exploiting the advantages of both 2D and 3D data. Some landmarks such as pupils are arduous to be automatically extracted only via three-dimensional techniques. Thus, this method is intended as a bridging-the-gap preliminary technique that takes advantages of 2D imaging only for integrating advanced landmark localization methodologies.
Keywords: Facial Landmarks; Landmark Localization; Face Analysis; RGB Images; Differential Geometry.
Gait Recognition Based on Model-Based Methods and Deep Belief Networks
by Benouis Mohamed, Senouci Mohamed, Tlemsani Redouane, Mostefai Lotfi
Abstract: The sensitivity to illumination variations, pose, gender, age, clothing and any another source of changes, can be one of the most important challenges, in gait recognition system. In this paper, we adopt many approaches to extract signatures of human body (static model) using a model-based method, such as static body parameters, ellipse-fitting and Robust shape coding. To reduce the dimension of this features set, a principal component analysis (PCA) technique is employed. Then, a deep belief networks classifier is used to classify the gait signatures. The performance of the deep belief network (DBN) is superior to other classifiers such as k-nearest neighbor (KNN) and dynamic times warping (DTW). The comparison is performed for viewpoint changes, clothing and carrying conditions. The proposed approach has been validated on the Gait Database B.
Keywords: Biometrics; gait; model-based; model free; feature extraction; principal component analysis; k-nearest neighbour; dynamic times warping; deep belief network.