International Journal of Computer and Applications

Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions

Face recognition has become more significant and relevant in recent years owing to it potential applications. Since the faces are highly dynamic and pose more issues and challenges to solve, researchers in the domain of pattern recognition, computer vision and artificial intelligence have proposed many solutions to reduce such difficulties so as to improve the robustness and recognition accuracy. As many approaches have been proposed, efforts are also put in to provide an extensive survey of the methods developed over the years. The objective of this paper is to provide a survey of face recognition papers that appeared in the literature over the past decade under all severe conditions that were not discussed in the previous survey and to categorize them into meaningful approaches, viz. appearance based, feature based and soft computing based. A comparative study of merits and demerits of these approaches have been presented.

A review of face recognition methods

We develop a new approach of illumination normalization for face recognition under varying lighting conditions. The effect of illumination variations is in decreasing order over low-frequency discrete cosine transform (DCT) coefficients. The proposed approach is expected to nullify the effect of illumination variations as well as to preserve the low-frequency details of a face image in order to achieve a good recognition performance. This has been accomplished by using a fuzzy filter applied over the low-frequency DCT (LFDCT) coefficients. The ‘simple classification technique’ (k-nearest neighbor classification) is used to establish the performance improvement by present approach of illumination normalization under high and unpredictable illumination variations. Our fuzzy filter based illumination normalization approach achieves zero error rate on Yale face database B (named as Yale B database in this work) and CMU PIE database. An excellent performance is achieved on extended Yale B database. The present approach of illumination normalization is also tested on Yale face database which comprises of illumination variations together with expression variations and misalignment. Significant reduction in the error rate is achieved by the present approach on this database as well. These results establish the superiority of the proposed approach of illumination normalization, over the existing ones.

Illumination normalization using fuzzy filter in DCT domain for face recognition

In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: 1) uneven illumination; 2) partial occlusion; and 3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local area-based approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.

/pdf/largest-matching-areas-for-illumination-and-occlusion-robust-2c962b1zdw.pdf

Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

In this paper, a novel approach for illumination normalization under varying lighting conditions is presented. Our approach utilizes the fact that discrete cosine transform (DCT) low-frequency coefficients correspond to illumination variations in a digital image. Under varying illuminations, the images captured may have low contrast; initially we apply histogram equalization on these for contrast stretching. Then the low-frequency DCT coefficients are scaled down to compensate the illumination variations. The value of scaling down factor and the number of low-frequency DCT coefficients, which are to be re-scaled, are obtained experimentally. The classification is done using k-nearest neighbor classification and nearest mean classification on the images obtained by inverse DCT on the processed coefficients. The correlation coefficient and Euclidean distance obtained using principal component analysis are used as distance metrics in classification. We have tested our face recognition method using Yale face database B. The results show that our method performs without any error (100% face recognition performance) even on the most extreme illumination variations. There are different schemes in the literature for illumination normalization under varying lighting conditions, but no one is claimed to give 100% recognition rate under all illumination variations for this database. The proposed technique is computationally efficient and can easily be implemented for real time face recognition system.

/pdf/an-illumination-invariant-accurate-face-recognition-with-3510xtz9ft.pdf

An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients

A new approach for illumination normalization that is robust under varying lighting conditions is presented in this paper. For illumination normalization discrete cosine transform (DCT) is applied on the images captured under varying lighting conditions. Most of the illumination variations as well as various facial features of a human face correspond to low frequency region. A proper down scaling of low frequency DCT coefficients is utilized in our approach of illumination normalization. The images captured under varying illumination have low contrast. For contrast stretching the adaptive histogram equalization is applied on face images. For further enhancement of the images, logarithm transform is employed. To analyze our illumination normalization method, we have used Yale Face Database B. The results show better appearance of images and have been discussed with other approaches.

Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization

high dimensional pattern recognition problems, the learning speed of gradient based training algorithms (back-propagation) is generally very slow. Local minimum, improper learning rate and over-fitting are some of the other issues. Extreme learning machine was proposed as a non-iterative learning algorithm for single-hidden layer feed forward neural network (SLFN) to overcome these issues. The input weight and biases are chosen randomly in ELM which makes the classification system of non- deterministic behavior. In this paper, a new learning algorithm is presented in which the input weights and the hidden layer biases of SLFN are assigned from basis vectors generated by training space. The output weights and biases are decided through simple generalized inverse operation on output matrix of hidden layer. This makes very fast learning speed and better generalization performance in comparison to conventional learning algorithm as well as ELM.

/pdf/a-new-learning-algorithm-for-single-hidden-layer-feedforward-5fhftshwix.pdf

A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks

This paper brings out a new approach of information extraction based on fuzzy logic, which can be used for robust face recognition system. We have applied a fuzzification operation to extract the pixel wise association of face images to different classes. The fuzzification operation uses  membership function to obtain the degree of belonging of a particular pixel to all classes. Further nearest neighbor classification using correlation coefficient and principal component analysis are used to obtain the classification error over AT&T face database. The results clearly confirmed the superiority of proposed approach.

Fuzzy based Pixel wise Information Extraction for Face Recognition

We report a rare association of persistent truncus arteriosus with double aortic arch in a 34-day old neonate.

M. Gupta

Papers

An Illumination Invariant Accurate Face Recognition with Down Scaling of DCT Coefficients

Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization

A New Learning Algorithm for Single Hidden Layer Feedforward Neural Networks

Fuzzy based Pixel wise Information Extraction for Face Recognition

Persistent truncus arteriosus with double aortic arch.