The Bag Of Word Model: The Bag Of Words Model

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Abstract— Bag of words model (BOW),which was originally used for document classification has been introduced to computer vision field recently and used in object recognition successfully. The bag of words model to the face recognition which can robustly give excellent performance in recognizing the face under various conditions like illumination, pose variation ,facial expression and occlusions. The scale Invariant Feature Transform is used for extracting the features from the face and SVM classifiers are used for accurate classification of the gender. Experimental results shows that using this model can obtain the accurate classification of the gender which are invariant to illumination, occlusions ,pose variation and facial expression. Keywords—component; Bag Of Words; SIFT; SVM classifier
I. INTRODUCTION
Gender means the range of physical, mental and behavioral characteristics which differentiate the masculinity and femininity. Gender classification is a binary classification technique to classify male and female .Gender classification from a person’s face play an important role in variety of Computer vision based application. Automated facial gender recognition has become an interesting and challenging research problems in recent years
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In computer vision it is used in image classification, where the features are treated as words. The Bag of Words model consists of three steps i)Feature Extraction ii)Feature description iii)classification. Using this model the key-point are extracted from the face images and the extracted key-points are converted into 128 dimensional vectors. The experiment consists of two stages i)Testing ii)Training. During the training stage all the 128 dimensional vectors are trained by using the SVM classifier. The testing faces are directly applied to the classifier in order to recognize the face

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