A Generalized Neuron Model (GNM) Based Human Age Estimation

Abstract

In real-world application, the identification characteristic of face images has been widely explored, ranging from National ID card, International passport, driving license amongst others. In spite of the numerous investigation of person identification from face images, there exists only a limited amount of research on detecting and estimating the demographic information contained in face images such as age, gender, and ethnicity. This research aim at detecting the age/age range of individual based on the facial image. In this research, a generalized neuron (GN), which is a modification of the simple neuron, is used, to overcome some of the problems of artificial neural network (ANN) and improve its training and testing performance. The GN is trained with discrete wavelet transform (DWT) features obtained after the application of Canny edge detection algorithm on the face Image. Validating the technique on FGNET face images reveals that the frequency domain features obtained using the DWT captures the wrinkles on the face region, which represents a distinguishing factor on the face as humans grow older. The empirical results demonstrates that the GN outperforms the simple neuron, with detection rate of 93.5%, training time of 96.30secs, matching time of 14secs and root mean square error of 0.0523. The experimental results suggest that the GN model performs comparably and could be adopted for detecting human ages. Keywords: Estimation, Detection, Generalized Neuron Model (GNM), Human Age & Frequency Aims Research Journal Reference Format: Babatunde, R.S. & Yakubu, I.A. (2017): A Generalized Neuron Model (GNM) Based Human Age Estimation. Advances in Multidisciplinary & Scientific Research Journal. Vol. 3. No.3, Pp 65-72. dx.doi.org/10.22624/AIMS/V3N3P9