Gender Identification in Human Gait Using Neural Network

Abstract

Abstract-Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioural features of a person. In this paper gender recognition from human gait in image sequence have been successfully investigated. Silhouette of 15 males and 15 females from the database collected from CASIR site have been extracted. The computer vision based gender classification is then carried out on the basis of standard deviation, centre of mass and height from head to toe using Feed Forward Back Propagation Network with TRAINLM as training functions, LEARNGD as adaptation learning function and MSEREG as performance function. Experimental results demonstrate that the present gender recognition system achieve recognition performance of 93.4%, 94.6%, and 94.7% with 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons respectively. When the performance function is replaced with SSE the recognition performance is increased by 2%, 2.4% and 3% respectively for 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons.The above study indicates that Gait based gender recognition is one of the best reliable biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to quickly detect threats and provide differing levels of access to different user groups.

Simulation of Peak Ground Acceleration by Artificial Neural Network and Radial Basis Function Network

“…A general sahpe of multi-layer perceptron network has one or several more connections to other layers. Theoretically it can demonstrate every problem which can be proved by general feed forward network [9][10]. Also by testing, general feed forward network can solve the problem more accurately.…”

Odour Based Human Identification and Classification using Neural Networks

“…Diverse types of functions of neural network activation were then tested (e.g., Resilient backpropagation, Gradient descent backpropagation and Levenberg-Marquardt backpropagation) and numerous different topologies of neural network are tested with variations of neurons and hidden layers. Based on those relevant (VOCs) Volatile Organic Compounds, the proposed gender detection system produces accuracy percentage of 94.7%, 94.6%, 93.4% and with 4 layers/30 neurons, 3 layers/30 neurons and 2 layers/20 neurons respectively [6]. The factor of gender is one of the established demographic human being attributes; beside from gender there are many other demographic attributes such as ethnicity and age, which is identified via computer vision and is applied to several applications such as demographic studies, surveillance, human computer interaction, and biometrics [3,7,18].…”