Persian Sign Language Recognition Using Radial Distance and Fourier Transform
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I.J. Image, Graphics and Signal Processing, 2014, 1, 40-46 Published Online November 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2014.01.06 Persian Sign Language Recognition Using Radial Distance and Fourier Transform Bahare Jalilian Department of Computer Engineering, Kermanshah Science and Research Branch, Islamic Azad University Kermanshah, Iran [email protected] Abdolah Chalechale Department of Computer Engineering, Razi University Kermanshah, Iran [email protected] Abstract — This paper provides a novel hand gesture information. In gesture recognition, sign Language is recognition method to recognize 32 static signs of the translated for dynamic hand motions. In posture Persian Sign Language (PSL) alphabets. Accurate hand recognition, sign Language is recognized for static segmentation is the first and important step in sign alphabets and numbers. Sign language recognition language recognition systems. Here, we propose a system is useful for the hearing impaired to method for hand segmentation that helps to build a communicate with the normal people. better vision based sign language recognition system. Research on automatic sign recognition has been The proposed method is based on YCbCr color space, started since around 1995 [1]. Many different techniques single Gaussian model and Bayes rule. It detects region for hand gestures recognition have been analyzed, such of hand in complex background and non-uniform as fuzzy logic [2], Hidden Markov Models (HMMs) [3], illumination. Hand gesture features are extracted by neural networks [4, 5] and support vector machine [6]. radial distance and Fourier transform. Finally, the Tsai and Huang used Support Vector Machine (SVM) to Euclidean distance is used to compute the similarity recognize the static sign and apply HMM model to between the input signs and all training feature vectors identify the dynamic signs in the Taiwanese Sign in the database. The system is tested on 480 posture Language (TSL) [6]. Mehdi and Niaz khan [7] have images of the PSL, 15 images for each 32 signs. proposed an American Sign Language Recognition Experimental results show that our approach is capable based on sensor glove to capture the signs. to recognize all 32 PSL alphabets with 95.62% Artificial neural networks are used to recognize the recognition rate. sensor values coming from the sensor glove. Lee and Tasi have used 3D data and neural network to interpret Index Terms — Persian sign language, hand gesture Taiwan sign language [8]. Assaleh et al [9] have recognition, Guassian model, centroid distance, Fourier proposed the first continuous (Arabic Sign Language) transform, Euclidean distance ArSL system that was able to recognize ArSL using HMM. Hough transform and neural network have been used for recognition American Sign Language [10]. In [11] a system for Arabic Sign Language Recognition is I. INTRODUCTION designed that based on a Gaussian skin color model to Sign language is a non-verbal visual language that detect the signer's face and a Hidden Markov Model to used by deaf. In order to improve communication recognition signs. Wavelet transform and neural between deaf and hearing people, research in automatic network have been used for recognition dynamic signs sign language recognition is needed. One application of of American Sign Language [12]. sign language recognition systems are the use on A system for Arabic Sign Language Recognition is Internet because many deaf people are unable to use the provided which the input image is converted into World Wide Web and communicate by e-ma il in the YCbCr color space and a skin profile is used to detect way normal people do, since they commonly have great the skin color from the YCbCr image. Principal difficulties in reading and writing. The reason for this is Component Analysis algorithm is used to compose the that normal people learn and perceive written language feature vectors for signs and gestures library [13]. Kiani as a visual representation of spoken language. For deaf Sarkaleh et al [14] proposed a Persian sign language people, however, this correspondence does not exist, recognition system which is capable of recognizing 8 and letters—which encode phonemes—are just symbols Persian signs by discrete wavelet transform and a multi without any meaning. layered Perceptron (MLP) Neural Network (NN) to The major part of sign language is comprised of classify the selected images. In this system background gestures and postures hand that imply meaningful of all images was black. Karami et al [15] designed a Copyright © 2014 M ECS I.J. Image, Graphics and Signal Processing, 2014, 1, 40-46 Persian Sign Language Recognition Using Radial Distance and Fourier Transform 41 system to recognize static signs of the Persian Sign complex background with changing illumination Language (PSL) alphabets, background of all input condition. The hand segmentation method begins by images were uniform and black. Hand images are modeling human skin color in YCbCr color space using converted to grayscale images. Their system is based on a database of skin pixels. Skin color distribution is the wavelet transform and neural networks. modeled as a single Gaussian model. Similarly a non- Paulraj et al [16] introduced a very simple Malaysian skin or background model is built using a database of Sign Language recognition system based on the area of non-skin pixels. Thus a Skin Probability Image is the objects in a binary image and Discrete Cosine obtained in which the gray level of each pixel represents Transform (DCT) for extracting the features from the the probability of the corresponding pixel in the input video sign language. In preprocessing stage the movie image to represent skin. Next, hand gesture features are frames are converted into indexed image format and extracted by radial distance and Fourier transform. Average filter is applied on these images and the The rest of the paper is organized as fo llo ws. A detail unwanted noises are removed. A simple sign language description of the proposed PSL recognition system is recognition system was developed using the skin color presented in Section Ⅱ. In Section Ⅲ, we exhibit our segmentation, moment invariants for features extracting experimental results with discussion. Finally, the and neural network model [17]. In most of approaches, conclusions and further work are presented in Section input hand images have been assumed that have uniform Ⅳ . and plain background. The signs can be either static or dynamic. A static sign is a particular hand shape and pose which II. THE SUGGEST ED SYST EM represented by a single image. A dynamic sign is a moving gesture that represented by a sequence of hand The suggested system is provided to recognize 32 static sign of the Persian sign language (PSL) alphabets. images. The Persian sign language consists of These signs are shown in Figure 1.We assumed that the approximately 1075 gestures of the common alphabets and words that the basic Persian sign alphabet is input images include exactly one hand. Our system has two main phases: hand segmentation phase (hand region composed of 37 static and dynamic gestures [19]. Our detection) and the feature extraction phase. The block approach focuses on the recognition of 32 static hand images. In this paper, we propose a system to interpret diagram of the proposed recognition system is shown in Figure 2 and its main steps are discussed in the static gestures of alphabets in Persian sign language following. (PSL). First of all, an effective hand segmentation method is presented to detecting hand region in a Figure 1. The Persian Si gn Language Alphabets. region is necessary for image enhancement and for A. Hand Segmentation getting good results in sign language recognition Image Pre-processing and detection of the hand systems. The proposed system introduces a method for Copyright © 2014 M ECS I.J. Image, Graphics and Signal Processing, 2014, 1, 40-46 42 Persian Sign Language Recognition Using Radial Distance and Fourier Transform segmenting hands using YCbCr color space, single between skin and non-skin pixe ls to classify skin-pixel Gaussian model, Bayes rule and morphology operators. and to provide robust parameter against varying The block diagram of this phase is shown in Figure 3. illumination conditions. RGB values can be transformed to YCbCr color space using (1): Y 16 65.481 128.553 24.966 R (1) Cb = 128 + − 37.797 − 74.203 112.000 G Cr 128 112.000 − 93.786 −18.214B If only the chrominance component is used, segmentation of skin colored regions becomes powerful in this process. Therefore, the variations of luminance component are eliminated as much as possible by choosing the CbCr plane (chrominance components) of the YCbCr color space to build the model. Research has shown that skin color is clustered in a small region of the chrominance space [4], as shown in Figure 4. Figure 2. The Block Diagram of the Proposed Recognition Sy stem Figure 4. Skin color Distribution in YCbCr Color Space 2) Single Gaussian Model The skin color distribution in CbCr plane is modeled as a single Gaussian model. According to section 2.1 the reason for using a single Gaussian model is the localization of skin color to a small area in the CbCr chrominance space. This step begins with the modeling Figure 3. The Block Diagram of the Segmentation Phase in the of skin and non-skin color using a database of skin and Proposed Recognition System non-skin pixe ls, respectively. A database of labeled skin pixels is used to train the Gaussian model. Some of skin images from the database are shown in Figure 5. The 1) YCbCr Color Space Conversion mean and the covariance of the database characterize the One important factor that should be considered while model. Images containing human skin pixels as well as building a statistical model for color is the choice of a non-skin pixels are collected.