Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Performance of composite sand cement brick containing recycled concrete aggregate and waste polyethylene terepthalate

The reuse and recycling of waste materials from construction and demolition waste is one of the new concepts for brick manufacturing production. Construction and demolition debris refers to waste materials that result from the construction, renovation and demolition of buildings. Bricks are an important material for developing areas where manufacturers find it difficult to locate adequate sources due to the shortage of natural aggregate supply. Construction waste can be recycled to replace naturals resource or other competitive materials. This study aims to establish the sustainable properties for composite bricks using Recycle Concrete Aggregate (RCA) and Polyethylene Terephthalate (PET) waste bottles as sand aggregate replacement. RCA was obtained from crushed laboratory concrete cubes while PET bottles were collected around UTHM and Parit Raja areas. The objectives of this study are to determine the optimum cement-sand ratio (1:5, 1:6 and 1:7) for composite brick through density, compressive strength and water absorption tests, to investigate the mechanical properties and durability of composite sand cement bricks through shrinkage and carbonation tests, and to identify the optimum percentages of RCA and PET as sand aggregate replacement in composite bricks. For this study, the brick specimens were prepared using 25%, 50% and 75% of RCA and 1.0%, 1.5%, 2.0% and 2.5% of PET by volume of natural sand with a water-cement ratio of 0.6. The size of the RCA used measured less than 5 mm. Moreover, the size of the sieved waste PET granules was between 0.1 to 5 mm which made it physically similar to the size of fine aggregates. The bricks were cast in moulds measuring 215 mm in length, 103 mm in width, and 65 mm in depth. Three types of sand-cement ratios were used, namely 1:5, 1:6 and 1:7. The first stage of the study was the determination of the best sand-cement ratio through density, water absorption and compressive strength tests. The next stage was the determination of the optimum percentages of RCA and PET according to the shrinkage and carbonation tests. The overall results revealed that the best cement-sand ratio was 1:6. The density test indicates that the average density of composite bricks is lower compared to that of control bricks. The cement-sand ratio of 1:6 was the optimum value for all sample bricks. In addition, the percentage of water absorption for composite bricks was found to be satisfactory. It can be concluded that the optimal replacement of RCA and PET was R25P1 with a cement-sand ratio of 1:6 as it achieved the lowest values during the drying shrinkage and carbonation tests

DCTNet : A Simple Learning-free Approach for Face Recognition

PCANet was proposed as a lightweight deep learning network that mainly leverages Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization and block-wise histograming. PCANet was shown worked surprisingly well in various image classification tasks. However, PCANet is data-dependence hence inflexible. In this paper, we proposed a data-independence network, dubbed DCTNet for face recognition in which we adopt Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is motivated by the fact that 2D DCT basis is indeed a good approximation for high ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is free from learning as 2D DCT bases can be computed in advance. Besides that, we also proposed an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness. It is shown to provide surprising performance boost when the probe image is considerably different in appearance from the gallery image. We evaluate the performance of DCTNet extensively on a number of benchmark face databases and being able to achieve on par with or often better accuracy performance than PCANet.Comment: APSIPA ASC 201

A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods