This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are se...
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ISBN:
(纸本)9781479981748
This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.
In this paper, a technique for the recognition of unconstrained Arabic printed text is proposed. Features that measure the image characteristics at local scales are applied. A line image is divided into a set of one-p...
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In this paper, a technique for the recognition of unconstrained Arabic printed text is proposed. Features that measure the image characteristics at local scales are applied. A line image is divided into a set of one-pixel width windows which is sliding a cross that text line. Run length encoding is used to extract features from each window. A unique method is chosen to select best number of transitions for each window. The proposed recognition system is trained and tested on the APTI (Arabic Printed Text image) database. In order to select the optimal parameters for feature extraction and for the HMM classifier, the APTI training dataset is further divided into a smaller training subset and a verification set. The estimated parameters are, then, used in the testing phase. The presented technique provides state-of-the-art recognition results on the APTI database using HMMs. The achieved average recognition rates is 96.65% on the letter level using the HMM classifier.
This paper presents a cursive Arabic text recognition system. The system decomposes the document image into test line images and extracts a set of simple statistical features from a one-pixel width window which is...
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This paper presents a cursive Arabic text recognition system. The system decomposes the document image into test line images and extracts a set of simple statistical features from a one-pixel width window which is sliding a cross that text line. It then injects the resulting feature vectors to Hidden Markov Models. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computergenerated fonts.
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