An adaptive illumination normalization using non-linear regression for robust person identification under varying illuminations

The multifaceted light varying environment severely degrades the performance of person recognition using facial images. Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and fuzzy logic concepts. An efficient illumination normalization method is presented with the help of a new modified S membership function. The proposed method of illumination normalization retains the large scale facial features as well as suppresses the variations related to change in light variations. Kernel extreme learning machine (KELM) which is a nonlinear and non-iterative learning algorithm of ANN is used for classification. Various kernel types and parameters are experimented to find the best choice for robust classification. To assess the performance of proposed hybridization, Yale and extended Yale B face databases have been used. Very promising results have been achieved which establish the worth of the proposed method.

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.

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.

In order to extract sensitive features of face images from high dimensional image data and facilitate the recognition speed, this paper has proposed a novel method based on the manifold learning and genetic-chaos algorithm optimized kernel extreme learning machine (KELM) for the application of face recognition. The locally linear embedding (LLE) algorithm has been employed to extract distinct features by projecting the original high dimensionality of the face image into a low dimensionality space. Then the KELM is introduced to provide quick and accurate pattern recognition on the extracted features. The only parameter need to be determined in KELM is the neuron number of hidden layer. Literature review indicates that very limited work has addressed the optimization of this parameter. Hence, the genetic-chaos algorithm was used for the first time to optimize the KELM parameter in this paper. A robust KELM structure may be attained after the genetic-chaos optimization. In order to evaluate and verify the proposed method, experiment tests have been carried out using standard face expressions. The experimental analysis results indicate that the performance of the proposed LLE- genetic-chaos-KELM method outperforms its rivals in terms of both recognition accuracy and training speed. Index Terms—face recognition, feature extraction, GA Chaos, KELM.

Variations in illumination is one of major limiting factors of face recognition system performance. The effect of changes in the incident light on face images is analyzed, as well as its influence on the low frequency components of the image. Starting from this analysis, a new photometric normalization method for illumination invariant face recognition is presented. Low-frequency Discrete Cosine Transform coefficients in the logarithmic domain are used in a local way to reconstruct a slowly varying component of the face image which is caused by illumination. After smoothing, this component is subtracted from the original logarithmic image to compensate for illumination variations. Compared to other preprocessing algorithms, our method achieved a very good performance with a total error rate very similar to that produced by the best performing state-of-the-art algorithm. An in-depth analysis of the two preprocessing methods revealed notable differences in their behavior, which is exploited in a multiple classifier fusion framework to achieve further performance improvement. The superiority of the proposal is demonstrated in both face verification and identification experiments.

Non-stationary feature fusion of face and palmprint multimodal biometrics

Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms (GAs) as well as dimensionality reduction methods are utilized to create a combined framework of feature acquisition. In details, the face images are first transformed to frequency domain through DCT. Then GAs are used to seek for optimal features in the redundant DCT coefficients where the generalization performance guides the searching process. The last step is to reduce the dimension of selected features. In experiments, two face databases are used to evaluate the effectiveness of the proposed method. In addition, an entropy-based improvement is also proposed. The experimental results present the superiority of selected frequency features.

The launching process of multistage synchronous induction coilgun (MSSICG) is a complex process involving multifield coupling, so it is time-consuming to iteratively optimize its structural parameters by stochastic optimization algorithm. A nonlinear regression modeling method of MSSICG based on mainstream machine learning methods is proposed in this paper. Rooted in the current filament model (CFM) verified by prototype experiment, the sample set for training and testing could be obtained by a hybrid experimental design method. The least-squares support vector machine (LSSVM), the kernel extreme learning machine (KELM), and the eco state network (ESN) were employed to learn the training samples. In order to improve the accuracy of the prediction model, the chicken swarm optimization (CSO) was introduced to pretrain the hyperparameters of the LSSVM and the KELM as well as the parameters of the dynamic reservoir of the ESN. The results show that the predictive modeling of MSSICG based on CSO-KELM has better accuracy and generalization performance. Based on the obtained regression model, the CSO algorithm was used to optimize the structural parameters of a five-stage coilgun. It turns out that the optimization based on nonparametric model has higher computational efficiency than the optimization method which requires large-scale iterative calculation. This provides a novel idea for the engineering design of the MSSICG.

With advances in machine learning and development of neural networks that are efficient and accurate, this paper explores the use of kernel extreme learning machine (KELM) to develop a semi-blind watermarking technique for grey-scale images in discrete cosine transform domain. Fuzzy entropy is employed for selection of the blocks where the watermark bits are to be embedded. A dataset formed from these blocks is used to train KELM. The nonlinear regression property of KELM predicts the values where watermark bits are embedded. Self-adjustive differential evolution (SeAdDE) controls the strength of the scaling factors finds their optimal values. The adaptiveness of differential evolution (DE) helps in self-adjustment and varies the DE parameters to explore best solutions. This saves time as the manual hit and trial method for finding the appropriate parameter values is avoided. The scheme presented shows robustness against various attacks like histogram equalisation, resizing, JPEG compression, Weiner filtering, etc. and still also retains the quality of the watermarked image. Thus, the proposed technique can be used as a solution to ensure authenticity via watermarking.

This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.

A new illumination normalisation scheme based on reflectance ratio (RR) and contrast stretching (CS), feature extraction by integer wavelet transform (IWT) in fisher subspace for face recognition is proposed in this paper. RR is ratio of pixel intensity to average pixel intensity of its neighbourhood that discards illumination effects which is followed by CS to obtain further illuminated normalised images. Robust feature extraction is acquired by selecting low frequency coefficients and ignoring high frequency coefficients using IWT which makes proposed scheme computationally efficient. Illumination normalised feature extraction by combination of RR-CS and IWT model is followed by fisher linear discriminant analysis (FLDA) to achieve robust feature vector that provides best projection direction of training and test data sets. The recognition accuracy of 100% on CMU-PIE, Yale B and 99.05% (average) on extended Yale B face databases along with comparison with state of art methods proved strength of proposed model.

Indocyanine green (ICG) angiography is an imaging method for doctors to observe choroidal abnormalities in human eyes. The ICG angiograms typically exhibit inhomogeneous illumination, which poses serious difficulties for the development of computer-aided diagnostic tools. In this paper, we propose a novel illumination normalization method to alleviate the inhomogeneous illumination in ICG video angiograms. In particular, we first align the viewpoint of the input ICG video angiogram using an image registration method. Then, we acquire temporal information using time-dependent intrinsic image and compute the corresponding illumination image. Finally, we correct inhomogeneous illumination from the illumination image by estimating contrast and luminosity distortion. We have conducted extensive evaluation using ICG video angiograms of 60 patients. Two video quality assessment methods are utilized to evaluate the performance of our proposed illumination normalization method. The results show that our proposed method can help improve the visual quality of ICG video angiogram. Visual evaluation by a human expert also confirms that our method yields better illumination normalization results.

In this paper, an approach for illumination normalization based on DWT and DCT for face recognition under varying lighting conditions is presented. The wavelet transform decomposes the full image into one low-frequency and three high frequency sub bands. To compensate the illumination variations, it is required to suppress the low-frequency components. At the same time, the useful information from the low-frequency components has to be preserved for the purpose of discrimination between different face images. Therefore, in the presented approach, firstly, DWT is performed on the image and DCT Transform is employed on low-frequency sub band. Then Low-Frequency DCT (LFDCT) coefficients are modified to suppress the illumination variations. Finally, improved low-frequency band and rest three high frequency sub bands are combined using inverse DWT. Classification is performed using k Nearest Neighbor Classifier (k-NNC). To show the performance improvement of the given approach, experiments have been performed on Yale B database. Significant reduction in error rate has been achieved by the given approach of down scaling low-frequency DCT coefficients of low-frequency sub band of DWT.

This paper presents a new robust and invisible blind watermarking approach of colour images for copyright protection in hybrid DWT-DCT-SVD domain. In the proposed method, firstly the luminance component (Y) of the cover image is decomposed up to one level of discrete wavelet transform (DWT) coefficients and the low frequency band (LL) is transformed by discrete cosine transform (DCT). Then several selected low and intermediate frequency DCT coefficients of each block are extracted to generate a feature matrix and singular value decomposition (SVD) transform is applied to the feature matrix. Finally the watermark information is embedded by modifying the singular values of the feature matrix. Experimental results demonstrate that the proposed approach outperforms some popular existing watermarking methods in robustness against median filtering, Gaussian filtering, salt and pepper noise, average filtering, Gaussian noise, histogram equalisation, and so on, especially in case of lossy JPEG compression in addition to good imperceptibility.

Face recognition technology has evolved as a popular identification technique to perform verification of human identity. By using the feature extraction methods and dimensionality reduction techniques in the pattern recognition applications, a number of facial recognition systems has been produced with distinct measure of success. Various face recognition algorithms and their extensions, have been proposed in the past three decades. However, face recognition faces challenging problems in real life applications because of the variation in the illumination of the face images. In the recent years, the research is focused towards Illumination-invariant face recognition system and many approaches have been proposed. But, there are several issues in face recognition across illumination variation which still remains unsolved. This paper provides a research on an efficient illumination-invariant face recognition system using Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). For processing the illumination invariant image, low frequency components of DCT are used to normalize the illuminated image, odd and even components of DCT is used for compensation in illumination variation and PCA is used for recognition of face images. The existing approaches in illumination Invariant face recognition are comprehensively reviewed and discussed. The proposed approach is validated with Yale Face Database B. Experimental results demonstrate the effectiveness of this approach in the performance of face recognition.