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.
Machine Learning has been widely applied to various domains and has gained a lot of success. At present, various learning algorithms are available, still facing difficulties in choosing the best methods that can be applied to their data. In this paper we perform an empirical study on 9 individual learning algorithms on a dataset by analyzing their performances and provide some Rules-of-thumb on selecting the algorithm over the dataset. To evaluate the performance, here we suggested supervised learning algorithm which can compute faster and better over the defined set of algorithms based on Time Complexity and Confusion Matrix. To assess the results over the given dataset, Receiver Operating Characteristic (ROC) curve is plotted on a graph by sensitivity or recall. Finally, a structured way to evaluate the performance of supervised learning algorithms is proposed, as well as suggested which algorithm is best suitable for their data set by comparing the effectiveness of various algorithms.
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.
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