Fuzzy-based illumination normalization for face recognition

In this paper, a novel approach is presented for adaptive illumination normalization for face recognition under varying illuminations due to change in angle of light projection. Illumination normalization is performed over some of the low frequency discrete Cosine transform (DCT) coefficients which are computed adaptively based upon the significance of alteration of these coefficients. These are, then, modified using a non-linear modifier. The significance of the proposed approach is that the approach is adaptive in normalizing the illumination from the face images as the number of low frequency DCT coefficients that need to be modified using non-linear modifier are selected on the basis of variations of illumination present in the image. This variation in the illumination is also used in developing the non-linear modifier. The proposed approach is important in such a way that the level of illumination variations decides the computations needed i.e. lesser in comparison to the other existing state-of-art approaches as large number of frequency coefficients are not altered. The proposed approach is tested over various face databases: YALE B, Extended YALE B, CMU PIE, AR and YALE face database. The experimental results clearly reveal that the performance of the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, 100% accuracy is attained on all the subsets of YALE B, CMU PIE and on Subset 3 of Extended YALE B database. Promising results are achieved over remaining face databases as well.

Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time.Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm

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.

The upland mesic rainforests of eastern Australia have been described as a "mesothermal archipelago" where a chain of cool mountain "islands" arise from a warm "sea" of tropical and subtropical lowlands. An endemic freshwater crayfish belonging to the genus Euastacus is found on each of these mountain "islands." The Euastacus are particularly suitable for the study of evolution because each mountain harbors a unique species, there are many taxa present providing replication within the group and, most importantly, their distribution is linear, extending along a south-north axis. This group could have evolved by "simultaneous vicariance" where there was one vicariant separation event of a widespread ancestor, or by "south to north stepping stone dispersal" where there were long distance dispersal events from neighboring mountain islands, starting in the south and proceeding north in a dispersal-colonization wave. We used pairwise genetic distances between nearest geographic neighbors as a novel way to test the two hypotheses. If diversification was due to "south to north stepping stone dispersal," then pairwise genetic distances between nearest geographic neighbors should decrease progressively the farther north the taxon pairs are found, reflecting the decreasing periods of isolation. In this case there should be a negative correlation between the south to north rank order of nearest neighbors and pairwise genetic distances. A Spearman's correlation on 16S mtDNA pairwise genetic distances and geographic rank order was not significant, indicating there was no support for the south to north stepping stone dispersal hypothesis. If simultaneous vicariance was responsible for diversification then all nearest geographic neighbor taxon pairs should have similar genetic distances and, therefore, the variance in nearest neighbor distances should be zero, or close to it. To test if the observed variance was tending towards zero we developed a randomization test where nearest neighbor taxon pairs were assigned random genetic distances and the variances calculated. The observed variance lay in the < 0.05 range of the simulated variances, providing support for the simultaneous vicariance hypothesis. The data also suggest there was simultaneous vicariance of at least two ancestral Queensland lineages. The timing of this vicariant event was probably in the Pliocene, which is consistent with the divergence times reported for other Australian mesic rainforest restricted taxa.

The upland mesic rainforests of eastern Australia have been described as a “mesothermal archipelago” where a chain of cool mountain “islands” arise from a warm “sea” of tropical and subtropical lowlands. An endemic freshwater crayfish belonging to the genus Euastacus is found on each of these mountain “islands.” The Euastacus are particularly suitable for the study of evolution because each mountain harbors a unique species, there are many taxa present providing replication within the group and, most importantly, their distribution is linear, extending along a south-north axis. This group could have evolved by “simultaneous vicariance” where there was one vicariant separation event of a widespread ancestor, or by “south to north stepping stone dispersal” where there were long distance dispersal events from neighboring mountain islands, starting in the south and proceeding north in a dispersal-colonization wave. We used pairwise genetic distances between nearest geographic neighbors as a novel way to test the two hypotheses. If diversification was due to “south to north stepping stone dispersal,” then pairwise genetic distances between nearest geographic neighbors should decrease progressively the farther north the taxon pairs are found, reflecting the decreasing periods of isolation. In this case there should be a negative correlation between the south to north rank order of nearest neighbors and pairwise genetic distances. A Spearman's correlation on 16S mtDNA pairwise genetic distances and geographic rank order was not significant, indicating there was no support for the south to north stepping stone dispersal hypothesis. If simultaneous vicariance was responsible for diversification then all nearest geographic neighbor taxon pairs should have similar genetic distances and, therefore, the variance in nearest neighbor distances should be zero, or close to it. To test if the observed variance was tending towards zero we developed a randomization test where nearest neighbor taxon pairs were assigned random genetic distances and the variances calculated. The observed variance lay in the < 0.05 range of the simulated variances, providing support for the simultaneous vicariance hypothesis. The data also suggest there was simultaneous vicariance of at least two ancestral Queensland lineages. The timing of this vicariant event was probably in the Pliocene, which is consistent with the divergence times reported for other Australian mesic rainforest restricted taxa.

The fractional Fourier transform (FRFT) has the mixed time and frequency characteristics of signals. And FRFT is a powerful and effective tool for time-varying and nonstationary signal processing. To address face recognition problem, an approach based on 2D discrete fractional Fourier transform (2D-DFRFT) is proposed in this paper. First of all, the 2D-DFRFT of a facial image is computed. Then Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA) are combined to perform discriminative feature extraction. Then the nearest neighbor (NN) classifier is applied to perform face recognition, based on Euclidean distance. Experimental results on ORL face database indicate that the proposed approach obtains good recognition effects.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

As the most classical unsupervised dimension reduction algorithm, principal component analysis (PCA) has been widely used in hyperspectral images (HSIs) preprocessing and analysis tasks. Recently proposed superpixelwise PCA (SuperPCA) has shown promising accuracy where superpixels segmentation technique was first used to segment an HSI to various homogeneous regions and then PCA was adopted in each superpixel block to extract the local features. However, the local features could be ineffective due to the neglect of global information especially in some small homogeneous regions and/or in some large homogeneous regions with mixed ground truth objects. In this article, a novel spectral–spatial and SuperPCA (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -PCA) is proposed to learn the effective and low-dimensional features of HSIs. Inspired by SuperPCA we further adopt superpixels-based local reconstruction to filter the HSIs and use the PCA-based global features as the supplement of local features. It turns out that the global–local and spectral–spatial features can be well exploited. Specifically, each pixel of an HSI is reconstructed by the nearest neighbors’ pixels in the same superpixel block, which could eliminate the noise and enhance the spatial information adaptively. After the local reconstruction-based data preprocessing, PCA is performed on each region and the entire HSI to obtain local and global features, respectively. Then we simply concatenate them to get the global–local and spectral–spatial features for HSIs classification. The experimental results on two HSIs data sets demonstrate the superiority of the proposed method over the state-of-the-art methods. The source code of the proposed model is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XinweiJiang/S3-PCA</uri> .

Facial recognition technologies are increasingly employed in security and law enforcement due to advances in computer vision and machine learning. Despite these advancements, balancing recognition accuracy, computational efficiency, and data security remains a significant challenge in research. This paper proposes a secure and efficient framework for criminal identification that integrates facial keypoint detection, normalization, Principal Component Analysis (PCA), and cryptographic hashing. The approach begins with grayscale conversion and image size standardization, followed by the detection of facial keypoints using pre-trained models to localize discriminative facial features. Normalization ensures scale and pose invariance, while PCA reduces feature dimensionality, improving computational performance without compromising representational fidelity. The reduced feature vectors are subsequently transformed into cryptographic hash values, providing unique, tamper-resistant identifiers for individuals. Experimental results demonstrate that the proposed method achieves high identification accuracy and computational efficiency while maintaining robustness against variations in lighting conditions and facial expressions. In addition, we introduce a hybrid deep feature–PCA fusion stage, where compact embeddings from a lightweight CNN are combined with normalized keypoints before being subjected to secure hashing. This fusion enhances robustness against occlusions and adversarial variations, providing a modern and secure update to traditional biometric pipelines.

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

We present a technique to generate an illumination subspace for arbitrary 3D faces based on the statistics of measured illuminations under variable lighting conditions from many subjects. A bilinear model based on the higher-order singular value decomposition is used to create a compact illumination subspace given arbitrary shape parameters from a parametric 3D face model. Using a fitting procedure based on minimizing the distance of the input image to the dynamically changing illumination subspace, we reconstruct a shape-specific illumination subspace from a single photograph. We use the reconstructed illumination subspace in various face recognition experiments with variable lighting conditions and obtain accuracies which are very competitive with previous methods that require specific training sessions or multiple images of the subject

Human face recognition is still a challenging task for computer-based machines mainly due to the similarity of human faces and unpredictable variations such as illumination, pose, expression, occlusion etc. A lot of algorithms have been developed to deal with these challenging issues in face recognition. One of the difficult problems is the geometric variation of face images. In some face recognition applications, only face images with uncontrolled format are available. Face images obtained from face detection stage may be directly used. Therefore, alignment or normalization is required to achieve better recognition performance. Eye detection technique is normally used to normalize face images. Invariant representations are used to improve the generalization of facial features. Feature-based and modelbased automatic face alignment can also be used to localize human faces more precisely. Illumination variation is another tough problem because human face is a 3D model and practical lighting conditions are complicated and unpredictable. Illumination normalization based on preprocessing, illumination invariant feature extraction and face modelling have been employed to reduce illumination variations or render faces under varying lighting conditions. The geometric on-the-plane rotation of a face image can be normalized by aforementioned methods. However, a face image with rotation in depth is not easily adjusted to a certain view. It is difficult to recognize faces in all poses with only training face images in limited views. One solution is to use 2D or 3D generative models to generate novel views of the face. Another method is based on the view-subspace learning. For facial expression variations,

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.

Call for Papers: Special Issue ofComputer Vision and Image Understandingon the Analysis of Volumetric Images

Variations in lighting conditions, pose and expression make face recognition an even more challenging and difficult task. This paper presents a face recognition approach by using image enhancement and Gabor wavelets transformation. In face recognition, image preprocessing is the key step since it is important to features extract and recognition. Logarithm transformation and normalization are performed in face images captured under various lighting conditions for face recognition. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from Gabor filtered images. Significant improvements are also observed when the preprocessing and Gabor filtered images are used for feature extraction instead of the original images. The approach achieves 94.4% recognition accuracy using only 160 features of a face image. Experimental results show that the proposed approach improves face recognition performance using this scheme when training and testing on images captured under variable illumination and expression.