R. Mohammadian; M. Mahlouji; A. Shahidinejad
Abstract
Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. ...
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Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.
H.6. Pattern Recognition
A. Noruzi; M. Mahlouji; A. Shahidinejad
Abstract
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by him/her. Iris recognition (IR) is known to be the most reliable and accurate biometric identification system. The iris recognition system (IRS) consists of an automatic segmentation ...
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A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by him/her. Iris recognition (IR) is known to be the most reliable and accurate biometric identification system. The iris recognition system (IRS) consists of an automatic segmentation mechanism which is based on the Hough transform (HT). This paper presents a robust IRS in unconstrained environments. Through this method, first a photo is taken from the iris, then edge detection is done, later on a contrast adjustment is persecuted in pre-processing stage. Circular HT is subsequently utilized for localizing circular area of iris inner and outer boundaries. The purpose of this last stage is to find circles in imperfect image inputs. Also, through applying parabolic HT, boundaries are localized between upper and lower eyelids. The proposed method, in comparison with available IRSs, not only enjoys higher accuracy, but also competes with them in terms of processing time. Experimental results on images available in UBIRIS, CASIA and MMUI database show that the proposed method has an accuracy rate of 99.12%, 98.80% and 98.34%, respectively.
