Emotion Recognition Using Discrete Cosine Transform and Discrimination Power Analysis

International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 130 ISSN 2229-5518 Emotion Recognition using discrete cosine transform and discrimination power analysis Shikha Surendran Dept. of Electronics and Communication MIT, Anjarakandy, Kannur Kerala [email protected] ABSTRACT 1. INTRODUCTION The paper states the technique of recognizing A persons face changes according to emotion using facial expressions is a central emotions or internal states of the person. Face is a element in human interactions. Discrete cosine natural and powerful communication tool. transform (DCT) is a powerful transform to Analysis of facial expressions through moves of extract proper features for face recognition. The face muscles leads to various applications. Facial proposed technique uses video input as input expression recognition plays a significant role in image taken through the webcam in real time on Human Computer Interaction systems. Humans which discrete cosine transform is performed. can understand and interpret each other’s facial After applying DCT to the entire face images, changes and use this understanding to response some of the coefficients are selected to construct and communicate. A machine capable of feature vectors. Most of the conventional meaningful and responsive communication is one approaches select coefficients in a zigzag manner of the main focuses in robotics. There are many or by zonal masking. In some cases, the low- other areas which benefit from the advances in frequency coefficients are discarded in order to facial expression analysis such as psychiatry, compensate illumination variations. Since the psychology, educational software, video games, discrimination power of all the coefficients is not animations, lie detection or other practical real- the same and some of them are discriminant than time applications. A wide variety of approaches others, so we can achieve a higher true have been developed by researchers for face recognition rate by usingIJSER discriminant coefficients recognition problem. From one point of view, (DCs) as feature vectors. Discrimination power these various approaches are categorized into two analysis (DPA) is a statistical analysis based on general groups, namely feature-based and holistic the DCT coefficients properties and approach. In feature-based approaches ,shape sand discrimination concept. It searches for the geometrical relationship of the individual facial coefficients which have more power to features including eyes, mouth and nose are discriminate different classes better than others.. analyzed. But in holistic approaches, the face The proposed method can be implemented for any images are analyzed as two-dimensional holistic feature selection problem as well as DCT patterns. Feature-based approaches are more coefficients. One-against-one classification robust against rotation, scale and illumination method is adopted using these features. variations, but their success depends on the facial Evaluation of the procedure is performed in feature detection .Due to difficulties in facial MATLAB using an image database of 20 people feature detection, holistic approaches are and each subject have 6 different facial considered much more frequently than feature- expressions. After training about many epochs based approaches . Extracting proper features is system achieved approximately 94% accuracy. crucial for satisfactory design of any pattern classifier system. In this way, two types of Key Words: discrete transforms, statistical and deterministic, Discrimination power analysis (DPA), have been widely used for feature extraction and Discrete cosine transform (DCT), face data redundancy reduction. The basis vectors of recognition, Coefficient selection (CS). the statistical transforms depend on the statistical specification of the database and different basis vectors are possible for different databases. Deterministic transforms have invariant basis IJSER © 2016 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 131 ISSN 2229-5518 vectors which are independent of the database. Dimension of the DCT coefficient matrix is Although statistical transforms have a great ability the same as the input image. In fact the DCT, to remove correlation between data, they have by itself, does not decrease data dimension; so high computational complexity. Also computation it compresses most signal information in a of the basis vectors for each given database is small percent of coefficients needed. Among statistical approaches, principal component analysis (PCA) and linear discriminant analysis (LDA) are two powerful statistical tools .2.1. DCT and coefficients selection for feature extraction and data representation. For an M X N image, where each image Because of some limitations of the PCA and corresponds to a 2D matrix, DCT coefficients LDA, a variety of modifications have been are calculated as follows: proposed by authors. Combination of statistical F(u,v)= x(u)x(v) and deterministic transforms constructs a third type of feature extraction approaches with both )X advantages. In this type, DCT reduces the dimension of data to avoid singularity and decreases the computational cost of PCA and u = 0; 1; . ;M; v= 0; 1; . ;N LDA. Studies showed that using the PCA or the where x( is defined by LDA in the DCT domain yields the same results as the one obtained from the spatial domain. x( = Various combinations of the DCT, PCA and LDA have been surveyed. Some used a combination of the DCT, PCA and the characteristics of the human visual system for encoding and recognition F(x,y) is the image intensity function and f(u,v) is of faces. After applying the DCT to an image, a 2d matrix of dct coefficients .block-based some coefficients are selected and others are implementation and the entire image are the two discarded in data dimension reduction. The implementations of the dct . Entire image dct has selection of the DCT coefficients is an important been used in this paper. The dct is applied to the part of the feature extraction process. In most of entire image to obtain the frequency coefficient the approaches which utilize the DCT, not enough matrix of the same dimension. Fig. 1a and b attention was given to coefficients selection (CS). shows a typical face image and its dct coefficients The coefficients are usually selected with image. In general ,the dct coefficients are divided conventional methodsIJSER such as zigzag or zonal into three bands (sets), namely low frequencies, masking. These conventional approaches are not middle frequencies and high frequencies. Fig. 1c necessarily efficient in all the applications and for visualizes these bands. Low frequencies are all the databases. Discrimination power analysis correlated with the illumination conditions and (DPA) is a novel approach which selects features high frequencies represent noise and small (DCT coefficients) with respect to their variations. Middle frequencies coefficients discrimination power. DPA utilizes statistical contain useful information and construct the basic analysing of a database, associates each DCT structure of the image .from the above discussion, coefficient discrimination to a number, and itseems that the middle frequencies coefficients generates a CS mask. The proposed feature are more suitable candidates in face recognition. extraction approach is database dependent and is able to find the best discriminant coefficients for each database. 2. Feature extraction In this section, various DCT feature extraction approaches are considered and a new efficient approach is proposed. DCT feature extraction consists of two stages. In the first stage, the DCT is applied to the entire image to obtain the DCT coefficients, and then some of the coefficients are selected to 2.2. Basis of data-dependent approach construct feature vectors in the second stage. Conventional CS approaches select the IJSER © 2016 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 7, Issue 2, February-2016 132 ISSN 2229-5518 fixed elements of the DCT coefficients the limited number of the training samples. matrix. We call these conventional CS DPA has no singularity problem which is approaches deterministic. Zigzag, zonal common among LDA-based methods. DPA masking and their modifications are the can be used as a stand-alone feature reduction examples of the deterministic approaches. Two popular deterministic approaches are shown in Fig. 2. Although the deterministic approaches are simple, they are not necessarily efficient for all the databases. Using a DCT coefficient in a feature vector, which improves the true recognition rate in a database, may deteriorate results in another database. We claim that an adaptive CS method will improve the accurate recognition rate. In this way, we propose another group of CS approaches namely, data-dependent approaches. The main idea behind the data- dependent approaches is that all of the DCT 2.3. Discrimination power analysis coefficients do not have the same ability to DP of a coefficient depends on two discriminate various classes. In other words, attributes: large variation between the classes some coefficients, namely discriminant and small variation within the classes(and it coefficients, separate classes better than means large discrimination power).Thus in others. These discriminant coefficients are one possible way, the DP can be estimated by dependent on database specifications.

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