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Go to Editorial ManagerIris pattern is one of the most important biological traits of humans. In last years, the iris pattern is used for human verification because of uniqueness of its texture. In this paper, biometric system based iris recognition is designed and implemented using two comparative approaches. The first approach is the Fourier descriptors, in this method the iris features have been extracted in frequency domain, where the low spectrums define the general description of iris pattern, while the high spectrums describes the fine detail. The second approach, the principle component analysis uses statistic technique to select the most important feature values by reducing its dimensionality. The biometric system is tested by applying one-to-one pattern matching procedure for 50 persons. The distance measurement method is applied for Manhattan, Euclidean, and Cosine classifiers for purpose of comparison. In all three classification methods, Fourier descriptors were always advanced principle component analysis in matching results. It satisfied 96%, 94%, and 86% correct matching against 94%, 92%, and 80% for principle component analysis using Manhattan, Euclidean, and Cosine classifiers respectively.
The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.
Speaker recognition refers to identifying the speaker by his or her voice. People talk in a variety of tones and each speaking voice has features that distinguish one person from another. Speaker verification (SV)involves comparing a set of measures of the speaker’s utterances with a reference for the person whose identification is being asserted to accept or reject the speaker’s identity claim. An identity claim is made during speaker verification which consists of two steps: extraction of feature and matching of feature. In this work, the analysis of correlations of Mel-scale coefficients for the voice of utterance to identify the intended speaker is presented. Short text-dependent word and other text-independent word is represented in this study. The correlation accuracy ranged from 98% to 99% for user1 (same speaker) for text-dependent. whereas 83% and 61% for user1 correlation with other speakers for text-dependent and independent respectively. Furthermore, the MFCC feature extraction approach based on distributed Discrete Cosine Transform (DCT) is provided in this research. SV tests are carried out using the MFCC feature extractions method where close variance for the target speaker and away variance for other speakers is obtained. Additionally, the principle component analysis (PCA) is provided to improve the discriminative system performance. Where the PCA chooses the optimal path between every pair of extremely confusing speakers. The results obtained from PCA were similar to the correlation finding from the Mel-scale results with enhancing the discriminative information and with lowering the dimension of MFCCs data..
Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images. .