Anwar M. Mirza

Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems

A non-intrusive fatigue detection system based on the video analysis of drivers.Eye closure duration measured through eye state information and yawning analyzed through mouth state information.Lips are searched through spatial fuzzy c-means (s-FCM) clustering.Pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle.The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through Viola-Jones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender. The system yielded an average accuracy of 100% on all the videos on which it was tested.

Image forgery detection using multi-resolution Weber local descriptors

In this paper, a multi-resolution Weber law descriptors (WLD) based image forgery detection method is introduced. Due to the maturing of digital image processing techniques, there are many tools, which can edit an image easily without leaving obvious traces to the human eyes. So the authentication of digital images is an important issue in our life. The proposed multi-resolution WLD extracts the features from chrominance components, which can give more information that the human eyes cannot notice. A support vector machine is used for classification purpose. The experiments are conducted on a large image database designed for forgery detection. The experimental results show that the accuracy rate of the proposed method can reach up to 93.33 % with multi-resolution WLD descriptor on the chrominance space of the images.

Gender Recognition Using Fusion of Local and Global Facial Features

Human perception of the face involves the observation of both coarse (global) and detailed (local) features of the face to identify and categorize a person. Face categorization involves finding common visual cues, such as gender, race and age, which could be used as a precursor to a face recognition system to improve recognition rates. In this paper, we investigate the fusion of both global and local features for gender classification. Global features are obtained using the principal component analysis (PCA) and discrete cosine transformation (DCT) approaches. A spatial local binary pattern (LBP) approach augmented with a two-dimensional DCT approach has been used to find the local features. The performance of the proposed approach has been investigated through extensive experiments performed on FERET database. The proposed approach gives a recognition accuracy of 98.16% on FERET database. Comparisons with some of the existing techniques have shown a marked reduction in number of features used per image to produce results more efficiently and without loss of accuracy for gender classification.

Rician noise removal from MR images using novel adapted selective non-local means filter

The reduction of rician noise from MR images without degradation of the underlying image features has attracted much attention and has a strong potential in several application domains including medical image processing. Interpretation of MR images is difficult due to their tendency to gain rician noise during acquisition. In this work, we proposed a novel selective non-local means algorithm for noise suppression of MR images while preserving the image features as much as possible. We have used morphological gradient operators that separate the image high frequency areas from smooth areas. Later, we have applied novel selective NLM filter with optimal parameter values for different frequency regions of image to remove the noise. A method of selective weight matrix is also proposed to preserve the image features against smoothing. The results of experimentation performed using proposed adapted selective filter prove the soundness of the method. We compared results with the results of many well known techniques presented in literature like NLM with optimized parameters, wavelet based de-noising and anisotropic diffusion filter and discussed the improvements achieved.

Estimation and optimization based ill-posed inverse restoration using fuzzy logic

Intelligent systems ranging from neural network, evolutionary computations and swarm intelligence to fuzzy systems are extensively exploited by researchers to solve variety of problems. In this paper focus is on deblurring that is considered as an inverse problem. It becomes ill-posed when noise contaminates the blurry image. Hence the problem is very sensitive to small perturbation in data. Conventionally, smoothness constraints are considered as a remedy to cater the sensitivity of the problem. In this paper, fuzzy rule based regularization parameter estimation is proposed with quadratic functional smoothness constraint. For deblurring image in the presence of noise, a constrained least square error function is minimized by the steepest descent algorithm. Visual results and quantitative measurements show the efficiency and robustness of the proposed technique compared to the state of the art and recently proposed methods.

Copy move image forgery detection method using steerable pyramid transform and texture descriptor

In this paper, we propose a passive copy move image forgery detection method using a steerable pyramid transform (SPT) and Local Binary Pattern (LBP). SPT is applied on a grayscale version or one of the YCbCr channels of an image. LBP is applied to describe the texture in each SPT subband. Then the support vector machine (SVM) uses the LBP feature extracted from SPT sub-bands in classifying images into tampered or authentic images. Experimental results show an excellent effectiveness for the proposed method in some combinations of SPT sub-band.

GENDER RECOGNITION FROM FACE IMAGES WITH DYADIC WAVELET TRANSFORM AND LOCAL BINARY PATTERN

Gender recognition from facial images plays an important role in biometric applications. We investigated Dyadic wavelet Transform (DyWT) and Local Binary Pattern (LBP) for gender recognition in this paper. DyWT is a multi-scale image transformation technique that decomposes an image into a number of subbands which separate the features at different scales. On the other hand, LBP is a texture descriptor and represents the local information in a better way. Also, DyWT is a kind of translation invariant wavelet transform that has better potential for detection than DWT (Discrete Wavelet Transform). Employing both DyWT and LBP, we propose a new technique of face representation that performs better for gender recognition. DyWT is based on spline wavelets, we investigated a number of spline wavelets for finding the best spline wavelets for gender recognition. Through a large number of experiments performed on FERET database, we report the best combination of parameters for DyWT and LBP that results in maximum accuracy. The proposed system outperforms the stat-of-the-art gender recognition approaches; it achieves a recognition rate of 99.25% on FERET database.

RACE CLASSIFICATION FROM FACE IMAGES USING LOCAL DESCRIPTORS

This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more discriminative bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally).

Gender recognition from faces using bandlet and local binary patterns

In this paper, multi-scale bandlet and local binary pattern (LBP) based method for gender recognition from faces is proposed. Bandlet is one of the multi-resolution techniques that can adapt the orientation of the edges of the face images, and thereby can better capture the texture of a face image. After extracting bandlet coefficients from face images at different scales, LBP is applied to create a histogram, which is used as the feature to a minimum distance classifier. The experiments are performed using FERET grayscale face database, and the highest accuracy of 99.13% is obtained with the proposed method.

Copy-Move Image Forgery Detection Using Multi-Resolution Weber Descriptors

Due to the maturing of digital image processing techniques, there are many tools, which can edit an image easily without leaving obvious traces to the human eyes. So the authentication of digital images is an important issue in our life. In this paper, multi-resolution Weber law descriptors (WLD) based method that detects copy-move image forgery is introduced. The proposed multi-resolution WLD extracts the features from chrominance components, which can give more information that the human eyes cannot notice. A support vector machine is used for classification purpose. The experiments are conducted on a large image database designed for forgery detection. The experimental results show that the accuracy rate of the proposed method can reach up to 91 % with multi-resolution WLD descriptor on the chrominance space of the images.