Automatic recognition of facial expressions can be an important component of natural humanmachine interfaces; it may also be used in behavioural science and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. This paper, presents recognition of facial expression by integrating the features derived from Grey Level Co-occurrence Matrix (GLCM) with a new structural approach derived from distinct LBP's (DLBP) ona 3 x 3 First order Compressed Image (FCI). The proposed method precisely recognizes the 7 categories of expressions i.e.: neutral, happiness, sadness, surprise, anger, disgust and fear. The proposed method contains three phases. In the first phase each 5 x 5 sub image is compressed into a 3 x 3 sub image. The second phase derives two distinct LBP's (DLBP) using the Triangular patterns between the upper and lower parts of the 3 x 3 sub image. In the third phase GLCM is constructed based on the DLBP's and feature parameters are evaluated for precise facial expression recognition. The derived DLBP is effective because it integrated with GLCM and provides better classification performance. The proposed method overcomes the disadvantages of statistical and formal LBP methods in estimating the facial expressions. The experimental results demonstrate the effectiveness of the proposed method on facial expression recognition.
Invention of the digital camera and also cell phones with powerful cameras with moderate and low pricing system has given the common man the privilege to capture his world in pictures anywhere, at any time, and conveniently share them with others. This has resulted the generation of volumes of images. These factors have created numerous possibilities and finally created interest among the researchers towards the design of an efficient and accurate Content Based Information Retrieval (CBIR) system. That's why new technological advances and growth in CBIR has been unquestionably rapid during the last five years. Various face recognition methods are derived using local features, and among them the Local Binary Pattern (LBP) approach is very famous. The basic disadvantage of these methods is they completely fail in representing features derived from large or macro structures or regions, which are very much essential for faces. To address this present paper proposes a median based multi region LBP. The proposed median based multi region LBP, initially divides the facial image in to nonoverlapped regions of size 5 x 5. LBP values are evaluated by dividing the region in to sub regions of size 3 x 3. The 9 subregion LBP values are arranged in the sorted manner and the median LBP code is considered as the feature vector for the region. The present paper also proposes the minimum and maximum based regional LBP methods for efficient image retrieval. To overcome the noise and illumination effect the proposed method initially applied DOG preprocessing method with gamma correction. The proposed method is applied on FG-NET and Goggle databases for efficient facial image retrieval. The experimental results indicate the efficiency of the proposed method.
Texture is an important spatial feature useful for identifying objects or regions of interest in an image. The present paper derives a new set of texture features, which are morphological shape components derived from the fuzzy texture elements of a 3x3 mask. The proposed fuzzy texture element patterns (FTP's) extract textural information of an image with a more complete respect of texture characteristics in all the eight directions instead of only one displacement vector. The proposed FTP's retains discriminating power of texture elements. In the present paper, five simple morphological shape components are evaluated on each of the derived FTP. The experimental results on the five groups of texture images clearly show the efficacy and simplicity of the present method.
The two most popular statistical methods used to measure the textural information of images are the Grey Level Cooccurrence Matrix (GLCM) and Texture Units (TU) approaches. The novelty of the present paper is, it combines TU and GLCM features by deriving a new model called "Pattern based Second order Compressed Binary (PSCB) image" to classify human age in to four groups. The proposed PSCB model reduces the given 5 x 5 grey level image into a 2 x 2 binary image, while preserving the significant features of the texture. The proposed method intelligently compressed a 5x5 window into a 2x2 window and derived TU on them. Thus the derived TU also represents a TU of a 5x5 window. The TU of the proposed PSCB model ranges from 0 to 15, thus it overcomes the previous disadvantages in evaluating TU's.
In digital watermarking, an invisible signal referred as a watermark is embedded into multimedia data for various purposes such as copyright protection, fingerprinting, authentication etc. For applications where the availability of original data is essential, irreversible degradation of the original data is not acceptable and incurred distortions need to be removed. Examples of such applications include multimedia archives, military image processing, and medical image processing for electronic patient records (EPRs).High capacity watermarking is proposed in the paper and implemented using integer to integer wavelet transform. The proposed scheme divides an input image into non-overlapping blocks and embeds a watermark into the high frequency wavelet coefficients of each block. The conditions to avoid both underflow and overflow in the spatial domain are derived for an arbitrary wavelet and block size. The experimental results show that the implemented scheme achieves higher embedding capacity while maintaining distortion at a lower level than the existing invertible watermarking schemes.
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