Advances of Robust Subspace Face Recognition

Face recognition has been widely applied in fast video surveillance and security systems and smart home services in our daily lives. Over past years, subspace projection methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), are the well-known algorithms for face recognition. Recently, linear regression classification (LRC) is one of the most popular approaches through subspace projection optimizations. However, there are still many problems unsolved in severe conditions with different environments and various applications. In this chapter, the practical problems including partial occlusion, illumination variation, different expression, pose variation, and low resolution are addressed and solved by several improved subspace projection methods including robust linear regression classification (RLRC), ridge regression (RR), improved principal component regression (IPCR), unitary regression classification (URC), linear discriminant regression classification (LDRC), generalized linear regression classification (GLRC) and trimmed linear regression (TLR). Experimental results show that these methods can perform well and possess high robustness against problems of partial occlusion, illumination variation, different expression, pose variation and low resolution

Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces

Precise depth map upsampling and enhancement based on edge‐preserving fusion filters

A texture image plus its associated depth map is the simplest representation of a three‐dimensional image and video signals and can be further encoded for effective transmission. Since it contains fewer variations, a depth map can be coded with much lower resolution than a texture image. Furthermore, the resolution of depth capture devices is usually also lower. Thus, a low‐resolution depth map with possible noise requires appropriate interpolation to restore it to full resolution and remove noise. In this study, the authors propose potency guided upsampling and adaptive gradient fusion filters to enhance the erroneous depth maps. The proposed depth map enhancement system can successfully suppress noise, fill missing values, sharpen foreground objects, and smooth background regions simultaneously. Their experimental results show that the proposed methods perform better in terms of both visual and subjective metrics than the classic methods and achieve results that are visually comparable with those of some time‐consuming methods

A robust two‐stage face recognition system with localisation error compensation

In practical systems, face recognition under unconstrained conditions is a very challenging task, where their input images are first pre‐processed and initially aligned by a face detection algorithm. However, there are still some residual localisation errors after the initial alignment. If we do not take these errors into account, the recognition performance should be greatly degraded for most face recognition algorithms. Generally, when designing a practical face recognition system, we need to compromise the capability of residual error tolerance and the discriminating capability. Although it is feasible to apply an iterative alignment algorithm to fine‐tune alignment, it will increase the computation load significantly. In this study, we propose an adaptive two‐stage face recognition system consisting of two block‐based recognition stages with a relatively larger cell size (i.e. the size of local regions) in the first stage to provide sufficient tolerance for geometric errors followed by a smaller one in the second stage to accurately evaluate a most probable candidate subset, which is adaptively determined according to the proposed confidence measure. In addition, an iterative gradient‐based alignment algorithm is incorporated into the two‐stage system to refine the alignment such that the recognition performance can be improved and the computation load can be saved simultaneously

Histogram of gradient phases: a new local descriptor for face recognition

Gradient‐based local descriptors have received more attention these years and have been successfully used in many applications such as human detection and face recognition. The advantages of the local descriptors are the resistance to the local geometric and photometric errors and the robustness to the expression variations. In this paper, the authors propose a new local descriptor called the histogram of gradient phases (HGP), which has some intriguing properties compared with the existing local descriptors such as the histogram of orientated gradients and DAISY for face recognition under the unconstrained conditions. In contrast with the histogram of the oriented gradient descriptor, the orientation histogram is computed from the estimated gradient phase distributions instead of weighting the votes of the gradient magnitudes. In this paper, the phase distributions are estimated by means of the gradient phases and the variances are decided by the estimated gradient signal‐to‐noise ratios of the pixels in a local region. The HGP descriptor which takes the confidence of the gradient phase into account is more discriminative and less sensitive to the normalisation process than most local descriptors, which significantly degrade without a proper normalisation. The simulation results show that the proposed HGP descriptor achieves a better performance and is more robust than the existing local descriptors

Using statistical characteristics of gradient phases for robust face recognition under illumination variations

Gradient phase, which is treated as an illumination insensitive measure, is an important feature for visual detection and recognition applications, especially under illumination variations. However, fewer statistical characteristics of the gradient phase have been reported till now. First, the statistical characteristics of the gradient phase against gradient signal‐to‐noise ratios (gradient SNRs) were investigated. The analysed results show that the confidence (or standard deviation) of gradient phases against gradient SNRs should never be linearly related, as is usually supposed. With the help of the statistical analyses of the gradient phase, the gradient‐based visual detection and recognition were improved by incorporating confidence information into the cost function. Moreover, inspired by the analysed characteristics of the gradient phase, an enhanced gradientface method is proposed to improve the performance of the gradient phase‐based face recognition. Intensive simulations and comparisons are performed to show its superior performance without the side effect of discrimination loss that existed in some illumination normalisation approaches

Modified Hough Transforms for Object Feature Extraction *

In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly detect natural object parameters. Using designed test patterns and real images, simulations show that the proposed weighting functions are helpful in detecting noise-corrupted object features. Due to its robustness, the MHT can be easily figured with a coarse-to-fine adaptive search mechanism to reduce the huge amount of computation for feature parameters extraction

Low‐resolution face recognition in uses of multiple‐size discrete cosine transforms and selective Gaussian mixture models

Owing to losing the detailed information, the low‐resolution problem in face recognition degrades the recognition performance dramatically. To overcome this problem, a novel face‐recognition system has been proposed, consisting of the extracted feature vectors from the multiple‐size discrete cosine transforms (mDCTs) and the recognition mechanism with selective Gaussian mixture models (sGMMs). The mDCT could extract enough visual features from low‐resolution face images while the sGMM could exclude unreliable observation features in recognition phase. Thus, the mDCT and the sGMM can greatly improve recognition rate at low‐resolution conditions. Experiments are carried out on George Tech and AR facial databases in 16 × 16 and 12 × 12 pixels resolution. The results show that the proposed system achieves better performance than the existing methods for low‐resolution face recognition