Improve Face Recognition Performance Research Articles

Maximum margin sparse representation discriminative mapping with application to face recognition

Sparse subspace learning has drawn more and more attention recently. We propose a novel sparse subspace learning algorithm called maximum margin sparse representation discriminative mapping (MSRDM), which adds the discriminative information into sparse neighborhood preservation. Based on combination of maximum margin discriminant criterion and sparse representation, MSRDM can preserve both local geometry structure and classification information. MSRDM can avoid the small sample size problem in face recognition naturally and the computation is efficient. To improve face recognition performance, we propose to integrate Gabor-like complex wavelet and natural image features by complex vectors as input features of MSRDM. Experimental results on ORL, UMIST, Yale, and PIE face databases demonstrate the effectiveness of the proposed face recognition method.

Face recognition performance with superresolution

With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.

Extracting discriminative color features for face recognition

This paper presents a discriminative color features (DCF) method, which applies a simple yet effective color model, a novel similarity measure, and effective color feature extraction methods, for improving face recognition performance. First, the new color model is constructed according to the principle of Ockham’s razor from a number of available models that take advantage of the subtraction of the primary colors for boosting pattern recognition performance. Second, the novel similarity measure integrates both the angular and the distance information for improving upon the broadly applied similarity measures. Finally, the discriminative color features are extracted from a compact color image representation by means of discriminant analysis with enhanced generalization capabilities. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show the feasibility of the proposed method.

A comparative study of preprocessing mismatch effects in color image based face recognition

Face color information can play an important role in face recognition (FR) and it can be used to considerably improve FR performance obtained using only grayscale images. The color-based FR methods involve a preprocessing step where a color image is converted into either a monochromatic image or an image having a different color representation. In practical FR systems, the recording or transmission format of the testing images may be arbitrary or inconsistent depending on the application (e.g., face images could consist of grayscale or color pixels either in compressed or uncompressed form). Further, a wide variety of grayscale and color conversions can be used in the preprocessing step. This could lead to a so-called preprocessing mismatch in color-based FR methods: the training and testing face images, generated after preprocessing, do not match in terms of their degree of compression or in terms of their grayscale or color representations. In contrast to grayscale-based FR, a practical color-based FR system has a higher chance of being confronted with a preprocessing mismatch. The aim of this paper is to present a comparative study that addresses the impact of a preprocessing mismatch on color-image based FR methods. We explore three different types of preprocessing mismatches, which practical color-based FR system are highly likely to encounter. In addition, comparative and extensive experiments have been carried out to analyze the effects of the preprocessing mismatches on an FR performance, using Color FRETET, CMU-PIE, AR, and SCface public face databases. The important conclusions drawn from our experiments include: (1) of all color-based FR methods under consideration, color-based FR using feature-level fusion is the most robust approach to preprocessing mismatches; (2) the preprocessing mismatch caused by the use of compressed color images can significantly deteriorate FR performance of color-based FR methods; (3) grayscale testing images can be critical for the feasibility of color-based FR using an input-level fusion; (4) the preprocessing mismatch in terms of grayscale representation has little effect on the FR performances of color-based FR methods.

Glucose effects on long-term memory performance: duration and domain specificity

Previous research has suggested that long-term verbal declarative memory is particularly sensitive to enhancement by glucose loading; however, investigation of glucose effects on certain memory domains has hitherto been neglected. Therefore, domain specificity of glucose effects merits further elucidation. The aim of the present research was to provide a more comprehensive investigation of the possible effects of glucose administration on different aspects of memory by 1) contrasting the effect of glucose administration on different memory domains (implicit/explicit memory; verbal/non-verbal memory, and recognition/familiarity processes), 2) investigating whether potential effects on memory domains differ depending on the dose of glucose administered (25 g versus 60 g), 3) exploring the duration of the glucose facilitation effect (assessment of memory performance 35 min and 1 week after encoding). A double-blind between-subjects design was used to test the effects of administration of 25 and 60 g glucose on memory performance. Implicit memory was improved following administration of 60 g of glucose. Glucose supplementation failed to improve face recognition performance but significantly improved performance of word recall and recognition following administration of 60 g of glucose. However, effects were not maintained 1 week following encoding. Improved implicit memory performance following glucose administration has not been reported before. Furthermore, the current data tentatively suggest that level of processing may determine the required glucose dosage to demonstrate memory improvement and that higher dosages may be able to exert effects on memory pertaining to both hippocampal and non-hippocampal brain regions.

Fusion of color, local spatial and global frequency information for face recognition

This paper presents a novel face recognition method by means of fusing color, local spatial and global frequency information. Specifically, the proposed method fuses the multiple features derived from a hybrid color space, the Gabor image representation, the local binary patterns (LBP), and the discrete cosine transform (DCT) of the input image. The novelty of this paper is threefold. First, a hybrid color space, the RC r Q color space, is constructed by combining the R component image of the RGB color space and the chromatic component images, C r and Q, of the YC b C r and YIQ color spaces, respectively. The RC r Q hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RC r Q hybrid color space to extract features: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the C r component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RC r Q hybrid color space are fused using a weighted sum rule. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that the proposed method improves face recognition performance significantly. In particular, the proposed method achieves the face verification rate (ROC III curve) of 92.43%, at the false accept rate of 0.1%, compared to the FRGC baseline performance of 11.86% face verification rate at the same false accept rate.

A Hybrid Color and Frequency Features Method for Face Recognition

This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQ color space, which combines the R component image of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.

Fusing continuous spectral images for face recognition under indoor and outdoor illuminants

Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment and rank-based decision level fusion, for spectral face images are proposed for improving face recognition performance compared to conventional images. A new multispectral imaging system is briefly presented which can acquire continuous spectral face images for our concept proof with fine spectral resolution in the visible spectrum. Several experiments are designed and validated by calculating the cumulative match characteristics of probe sets via the well-known recognition engine-FaceIt®. Experimental results demonstrate that proposed fusion methods outperform conventional images when gallery and probes are acquired under different illuminations and with different time lapses. In the case where probe images are acquired outdoors under different daylight situations, the fused images outperform conventional images by up to 78%.

Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance

This paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques. In particular, the framework integrates Gabor image representation, a novel multiclass Kernel Fisher Analysis (KFA) method, and fractional power polynomial models for improving pattern recognition performance. Gabor image representation, which increases dimensionality by incorporating Gabor filters with different scales and orientations, is characterized by spatial frequency, spatial locality, and orientational selectivity for coping with image variabilities such as illumination variations. The KFA method first performs nonlinear mapping from the input space to a high-dimensional feature space, and then implements the multiclass Fisher discriminant analysis in the feature space. The significance of the nonlinear mapping is that it increases the discriminating power of the KFA method, which is linear in the feature space but nonlinear in the input space. The novelty of the KFA method comes from the fact that 1) it extends the two-class kernel Fisher methods by addressing multiclass pattern classification problems and 2) it improves upon the traditional Generalized Discriminant Analysis (GDA) method by deriving a unique solution (compared to the GDA solution, which is not unique). The fractional power polynomial models further improve performance of the proposed pattern recognition framework. Experiments on face recognition using both the FERET database and the FRGC (Face Recognition Grand Challenge) databases show the feasibility of the proposed framework. In particular, experimental results using the FERET database show that the KFA method performs better than the GDA method and the fractional power polynomial models help both the KFA method and the GDA method improve their face recognition performance. Experimental results using the FRGC databases show that the proposed pattern recognition framework improves face recognition performance upon the BEE baseline algorithm and the LDA-based baseline algorithm by large margins.

Supervised kernel locality preserving projections for face recognition

Subspace analysis is an effective approach for face recognition. Finding a suitable low-dimensional subspace is a key step of subspace analysis, for it has a direct effect on recognition performance. In this paper, a novel subspace method, named supervised kernel locality preserving projections (SKLPP), is proposed for face recognition, in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of real face images are represented by nonlinear kernel mapping. SKLPP cannot only gain a perfect approximation of face manifold, but also enhance local within-class relations. Experimental results show that the proposed method can improve face recognition performance.

Glucose enhancement of face recognition is unaffected by alterations of face features

Glucose improves memory on a variety of tasks has been well established in both the animal and human literature. Of particular relevance to the present experiment is the finding that administration of glucose improves face recognition performance in both healthy, young adults ( Metzger, 2000) and in Alzheimer’s patients ( Manning, Ragozzino, & Gold, 1993). This experiment attempted to expand on these findings to determine if glucose-enhanced performance for face recognition is specific to certain face features (e.g., nose, eyes, or hair). In a between subjects experiment that tested healthy, young adults, a pre-training administration of glucose (50 g) was shown to improve face recognition performance relative to placebo (27.3 mg saccharin); however, the significant improvement on the task was not specific to a particular feature. This result suggests that glucose did not enhance memory for individual face features, but rather enhanced overall memory for face stimuli.

Simple 1-D image enhancement for head-mounted low vision aid

High-contrast binary images (black and white without shades of gray) were demonstrated to improve face recognition performance by the visually impaired using static images (Peli et al., Invest Ophthalmol Vis Sci 1991;32:2337-2350). A binary head-mounted display device has now been adapted to present video in real time for testing as a low vision mobility aid (an electronic telescope). When used for text presentation (as a head-mounted CCTV), a single threshold produces satisfactory performance. However, binarization of a video signal through a single threshold results in high-contrast but extremely poor image detail. A more detailed binary image can be obtained by applying bandpass filtering prior to thresholding. The application of the adaptive 2-D enhancement technique used by the DigiVision device enables such live bandpass processing and provides substantial improvement. To reduce the cost, weight, and power consumption of a portable low vision aid, a one dimensional (1-D) analog video processing alte...

Encoding Activity and Face Recognition

A series of studies conducted over the past 20 years have explored the effects of various tasks on recognition memory for faces. Memory for faces appears better when the study task involves judgements about an abstract trait rather than a physical feature. The various situations in which these results were obtained raise important methodological questions regarding the learning conditions, whether incidental or intentional, and the duration of exposure to the stimulus during the study phase. We consider here two alternative explanations for the reported results. One concerns depth of processing and the other the opposition between component and holistic processing. Possible strategies for improving face recognition performance are considered.