Mohamed Rizon

Japanese Face Emotions Classification Using LIP Features

In this paper, lip features are applied to classify the human emotion using a set of irregular ellipse fitting equations using Genetic algorithm. As Japanese, is considered in this study. All six universally accepted emotions are considered for classifications. Lip is usually considered as one of the features for recognizing the emotion. In this work, three feature extraction methods are proposed and their respective performances are compared for determining the feature of the lips. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips. GA is adopted to optimize such irregular ellipse characteristics of the lip features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotion. This has given reasonably successful emotion classifications for Japanese subject.

A comparison of circular object detection using Hough transform and chord intersection

In this paper, the circular Hough transform (CHT) and the chord intersection have been used to find the circular object in the feature extraction process. The chord intersection technique does not require any gradient information which may be sensitive to noise meanwhile for the CHT technique, the gradient information has been used. In this research, the coconut was selected as the object of interest. 40 images have been experimented to evaluate the performance of the techniques and the detection rate for the CHT is 92.5% and 85% for the chord intersection technique. The average computational time for chord intersection technique is 0.1495s by CPU (AMD Athlon 64x2 Dual core 3800) 2GHz meanwhile CHT consumed more time, 2.3871s in detecting the circular pattern.

Multiple neural networks for Human Face Recognition

In this paper, a computational model has been developed to identify a face of an unknown person’s by utilizing eigenfaces as unique features and backpropagation Neural Network for recognition. The features of a basic human face are extracted using eigenfaces. These features are then used to identify an unknown face by using multiple numbers of backpropagation neural networks. Samples of 15 human faces are obtained from The ORL database. The experiments are compared to the effects of changes size of face images, different face images combination and different neural network parameter. The classification more than 90% for trained classes and 18% for untrained classes were achieved.

Personalized Face Emotion Classification Using Optimized Data of Three Features

In this paper, lip and eye features are applied to classify the human emotion through a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA). South East Asian face is considered in this study. All six universally accepted emotions and one neutral are considered for classifications. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to an unique characteristic of lips and eye. GA is adopted to optimize irregular ellipse and regular ellipse characteristics of the lip and eye features in each emotion respectively. The GA method approach has achieved reasonably successful classification of emotion. While performing classification, optimized values can mess or overlap with other emotions range. In order to overcome the overlapping problem between the emotions and at the same time to improve the classification, a neural network (NN) approach is implemented. The GA-NN based process exhibits a range of 83% - 90% classification of the emotion from the optimized feature of top lip, bottom lip and eye.

Fuzzy clustering for genetic algorithm based optimized ellipse data in classifying face emotion

In this paper, lip and eye features are applied to classify the human emotion using a set of irregular and regular ellipse fitting equations using genetic algorithm (GA). A South East Asian face is considered in this study. The parameters relating the face emotions, in either case, are entirely different. All six universally accepted emotions and one neutral are considered for classifications. The method which is fastest in extracting lip and eye features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips and eyes. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. One ellipse based fitness function is proposed for the eye configuration. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters.

Determination of mango fruit from binary image using randomized Hough transform

A method of detecting mango fruit from RGB input image is proposed in this research. From the input image, the image is processed to obtain the binary image using the texture analysis and morphological operations (dilation and erosion). Later, the Randomized Hough Transform (RHT) method is used to find the best ellipse fits to each binary region. By using the texture analysis, the system can detect the mango fruit that is partially overlapped with each other and mango fruit that is partially occluded by the leaves. The combination of texture analysis and morphological operator can isolate the partially overlapped fruit and fruit that are partially occluded by leaves. The parameters derived from RHT method was used to calculate the center of the ellipse. The center of the ellipse acts as the gripping point for the fruit picking robot. As the results, the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.