Ayan Seal

Comparative study of human thermal face recognition based on Haar wavelet transform and local binary pattern

Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database.

Minutiae based thermal face recognition using blood perfusion data

This paper describes an efficient approach for human face recognition based on blood perfusion data from infra-red face images. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to optical image data. Blood perfusion data are related to distribution of blood vessels under the face skin. A distribution of blood vessels are unique for each person and as a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. For classification, a five layer feed-forward backpropagation neural network has been used. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block sizes. Experiments have been performed on the database created at our own laboratory. The maximum success of 91.47% recognition has been achieved with block size 8×8.

Automated thermal face recognition based on minutiae extraction

In this paper, an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, sobel edge operators. A five layer feed-forward back propagation neural network is used as the classification tool. It has been found that the first method supercedes the other two producing an accuracy of 97.62% with block size 16×16 for bit-plane 4.

Minutiae from Bit-Plane Sliced Thermal Images for Human Face Recognition

In this paper an efficient approach for human face recognition based on the use of minutiae points is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing technologies are used to pre-process the captured thermogram. Then different physiological features are extracted using bit-plane slicing from the captured thermogram. These extracted features are called blood perfusion data. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to static image data. Blood perfusion data are related to distribution of blood vessels under the face skin. Distribution of blood vessels is unique for each person and as set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal consequence blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block size. Experiments have been performed on the database created at our own laboratory. The maximum success of 95.24% recognition has been achieved with block size 8×8 and 32×32 with bit-plane 4 and accuracy rate of 97.62% has been achieved with block size 16×16 for bit-plane 4.

Feature Selection using Particle Swarm Optimization for Thermal Face Recognition

This paper presents an algorithm for feature selection based on particle swarm optimization (PSO) for thermal face recognition. The total algorithm goes through many steps. In the very first step, thermal human face image is preprocessed and cropping of the facial region from the entire image is done. In the next step, scale invariant feature transform (SIFT) is used to extract the features from the cropped face region. The features obtained by SIFT are invariant to object rotation and scale. But some irrelevant and noisy features could be produced with the actual features. Unwanted features have to be removed. In other words, optimum features have to be selected for better recognition accuracy. The PSO helps to identify the optimum features set using local as well as global searches. Here, this process has been implemented to select a subset of features that effectively represents original feature extracted for better classification convergence. Finally, minimum distance classifier is used to find the class label of each testing images. Minimum distance classifier acts as an objective function for PSO. In this work, all the experiments have been performed on UGC-JU thermal face database. The maximum success rate of 98.61 % recognition has been achieved using SIFT and PSO for frontal face images and 90.28 % for all images.

Thermal Human Face Recognition Based on Haar Wavelet Transform and Series Matching Technique

Thermal infrared (IR) images represent the heat patterns emitted from hot object and they don’t consider the energies reflected from an object. Objects living or non-living emit different amounts of IR energy according to their body temperature and characteristics. Humans are homoeothermic and hence capable of maintaining constant temperature under different surrounding temperature. Face recognition from thermal (IR) images should focus on changes of temperature on facial blood vessels. These temperature changes can be regarded as texture features of images and wavelet transform is a very good tool to analyze multi-scale and multi-directional texture. Wavelet transform is also used for image dimensionality reduction, by removing redundancies and preserving original features of the image. The sizes of the facial images are normally large. So, the wavelet transform is used before image similarity is measured. Therefore, this paper describes an efficient approach of human face recognition based on wavelet transform from thermal IR images. The system consists of three steps. At the very first step, human thermal IR face image is preprocessed and the face region is only cropped from the entire image. Secondly, “Haar” wavelet is used to extract low frequency band from the cropped face region. Lastly, the image classification between the training images and the test images is done, which is based on low-frequency components. The proposed approach is tested on a number of human thermal infrared face images created at our own laboratory and “Terravic Facial IR Database”. Experimental results indicated that the thermal infra red face images can be recognized by the proposed system effectively. The maximum success of 95 % recognition has been achieved.

Fusion of Visible and Thermal Images Using a Directed Search Method for Face Recognition

A new image fusion algorithm based on the visible and thermal images for face recognition is presented in this paper. The new fusion algorithm derives the benefit from both the modalities images. T...

A FPGA based implementation of Sobel edge detection

A FPGA based architecture for Sobel edge detection algorithm is proposed.An 8-bit architecture is proposed to retrieve the addresses of pixels involved in convolution process.The proposed architectures reduce the time and space complexity compare to two existing architectures. This paper presents an architecture for Sobel edge detection on Field Programmable Gate Array (FPGA) board, which is inexpensive in terms of computation. Hardware implementation of the Sobel edge detection algorithm is chosen because hardware presents a good scope of parallelism over software. On the other hand, Sobel edge detection can work with less deterioration in high level of noise. A compact study is also been done based on the previous methods. The proposed architecture uses less number of logic gates with respect to previous method. Space complexity is also reduced using proposed architecture.

Illumination and Expression Invariant Face Recognition

An illumination and expression invariant face recognition method based on uniform local binary patterns (uLBP) and Legendre moments is proposed in this work. The proposed method exploits uLBP texture features and Legendre moments to make a feature representation with enhanced discriminating power. The input images are preprocessed to extract the face region and normalized. From normalized image, uLBP codes are extracted to obtain texture image which overcomes the effect of monotonic temperature changes. Legendre moments are computed from this texture image to get the required feature vector. Legendre moments conserve the spatial structure information of the texture image. The resultant feature vector is classified using k-nearest neighbor classifier with L1 norm. To evaluate the proposed method, experiments are performed on IRIS and NVIE databases. The proposed method is tested on both visible and infrared images under different illumination and expression variations and performance is compared with recen...

Selective block based approach for neoplasm detection from T2-weighted brain MRIs

A realistic challenge in neuroanatomy is to assist radiologists to detect the brain neoplasm at an early stage. This paper presents a fast and accurate Computer Aided Diagnosis (CAD) system based on selective block based approach for neoplasm (tumor) detection from T2-weighted brain MR images. The salient contribution of the presented work lies in a fast discrimination using selective block based approach. Local binary patterns are considered as features, which are trained by support vector machine. The experiments are performed on the dataset of 100 patients, in which 55 patients reported with brain tumor and rest as normal. The proposed CAD system achieves 99.67% accuracy with 100% sensitivity. The comparative studies on the same dataset report the outperformance of proposed CAD system by comparison with some of the existing system.