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Face Recognition Using the Discrete Cosine Transform

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Face Recognition Using the Discrete Cosine Transform International Journal of Computer Vision 43(3), 167–188, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Face Recognition Using the Discrete Cosine Transform ZIAD M. HAFED AND MARTIN D. LEVINE Center for Intelligent Machines, McGill University, 3480 University Street, Montreal, Canada H3A 2A7 [email protected] [email protected] Abstract. An accurate and robust face recognition system was developed and tested. This system exploits the feature extraction capabilities of the discrete cosine transform (DCT) and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. The method was tested on a variety of available face databases, including one collected at McGill University. The system was shown to perform very well when compared to other approaches. Keywords: Face recognition, discrete cosine transform, Karhunen-Loeve transform, geometric normalization, illumination normalization, feature extraction, data compression 1. Introduction This paper discusses a new computational approach to face recognition that, when combined with proper Face recognition by humans is a high level visual face localization techniques, has proved to be very task for which it has been extremely difficult to con- efficacious. struct detailed neurophysiological and psychophysi- This section begins with a survey of the face recog- cal models. This is because faces are complex natu- nition research performed to date. The proposed ap- ral stimuli that differ dramatically from the artificially proach is then presented along with its objectives and constructed data often used in both human and com- the motivations for choosing it. The section concludes puter vision research. Thus, developing a computa- with an overview of the structure of the paper. tional approach to face recognition can prove to be very difficult indeed. In fact, despite the many rel- atively successful attempts to implement computer- 1.1. Background and Related Work based face recognition systems, we have yet to see one which combines speed, accuracy, and robustness to face Most research on face recognition falls into two main variations caused by 3D pose, facial expressions, and categories (Chellappa et al., 1995): feature-based and aging. The primary difficulty in analyzing and recog- holistic. Feature-based approaches to face recognition nizing human faces arises because variations in a single basically rely on the detection and characterization of face can be very large, while variations between differ- individual facial features and their geometrical rela- ent faces are quite small. That is, there is an inherent tionships. Such features generally include the eyes, structure to a human face, but that structure exhibits nose, and mouth. The detection of faces and their fea- large variations due to the presence of a multitude of tures prior to performing verification or recognition muscles in a particular face. Given that recognizing makes these approaches robust to positional variations faces is critical for humans in their everyday activi- of the faces in the input image. Holistic or global ap- ties, automating this process would be very useful in a proaches to face recognition, on the other hand, involve wide range of applications including security, surveil- encoding the entire facial image and treating the re- lance, criminal identification, and video compression. sulting facial “code” as a point in a high-dimensional 168 Hafed and Levine space. Thus, they assume that all faces are constrained tem performed well for frontal mug shot images (Turk to particular positions, orientations, and scales. and Pentland, 1991). Specifically, it was tested on a Feature-based approaches were more predominant database of 16 individuals, but with several images per in the early attempts at automating the process of face person. These images covered changes in scale, orien- recognition. Some of this early work involved the use of tation, and lighting. The authors reported 96% correct very simple image processing techniques (such as edge classification over lighting variations, 85% over orien- detection, signatures, and so on) for detecting faces tation variations, and 64% over size variations. and their features (see, for example, Sakai et al., 1969; The KLT does not achieve adequate robustness Kelly, 1970). In Sakai et al. (1969), an edge map was against variations in face orientation, position, and il- first extracted from an input image and then matched to lumination (as seen in the above results). That is why a large oval template, with possible variations in posi- it is usually accompanied by further processing to im- tion and size. The presence of a face was then confirmed prove its performance. For example, in Akamatsu et al. by searching for edges at estimated locations of certain (1991), operations were added to the KLT method to features like the eyes and mouth. Kelly (1970) used an standardize faces with respect to position and size. improved edge detector involving heuristic planning Also, in Pentland et al. (1994), the authors still used to extract an accurate outline of a person’s head from the KLT, but now on particular features of a face. various backgrounds. These features became part of the “feature space,” and More recently, Govindaraju et al. (1990) proposed a a distance-to-feature-space (DFFS) metric was used to technique for locating a face in a cluttered image that locate them in an image (such localization could serve employed a deformable template similar to the ones as a pre-processing stage for later normalization, crop- used in Yuille et al. (1989). They based their template ping, and classification). A similar idea of using ‘local’ on the outline of the head and allowed it to deform ac- information was presented in Lades et al. (1993). An cording to certain spring-based models. This approach artificial neural network, which employed the so-called performed quite well when tested on a small data set, dynamic link architecture (DLA), was used to achieve but it sometimes gave rise to false alarms (Govindaraju distortion-invariant recognition. Local descriptors of et al., 1990). Other recent approaches have used hi- the input images were obtained using Gabor-based erarchical coarse-to-fine searches with template-based wavelets. By conveying frequency, position, and ori- matching criteria (Burt, 1989; Craw et al., 1992; entation information, this approach performed well on Shepherd, 1985). relatively large databases. Once a face has been located, its features must Yet another holistic approach to face recognition is be computed. Early examples of this are the work that based on linear discriminant analysis (LDA) (see of Kanade (1973) and Harmon (Harmon and Hunt, Swets and Weng, 1996; Belhumeur et al., 1997). In this 1977) who worked with facial profile features. An in- approach, Fisher’s linear discriminant (Duda and Hart, teresting recent discussion of feature-based methods, 1973) is used (on the space of feature vectors obtained which compares them to holistic approaches, is found by the KLT) to obtain the most discriminating features in Brunelli and Poggio (1993). of faces, rather than the most expressive ones given A successful holistic approach to face recognition by the KLT alone (Swets and Weng, 1996). In both uses the Karhunen-Loeve transform (KLT). This trans- Swets and Weng (1996) and Belhumeur et al. (1997), form exhibits pattern recognition properties that were LDA resulted in better classification than in the case of largely overlooked initially because of the complexity the KLT being applied alone, especially under varying involved in its computation (Chellappa et al., 1995). pose and illumination conditions. Kirby and Sirovich (1990) originally proposed the KLT As can be seen, there are merits to both feature- to characterize faces. This transform produces an ex- based and holistic approaches to face recognition, and pansion of an input image in terms of a set of ba- it seems that they may both be necessary to meet the two sis images or the so-called “eigenimages.” Turk and main objectives of a face recognition system: accuracy Pentland (1991) proposed a face recognition system and robustness. Holistic approaches may be accurate based on the KLT in which only a few KLT coefficients for simple frontal mug shots, but they must be accompa- were used to represent faces in what they termed “face nied by certain feature-based techniques to make them space.” Each set of KLT coefficients representing a face more robust. In fact, this may be true for humans as formed a point in this high-dimensional space. The sys- well. Both holistic information and feature information Face Recognition Using the Discrete Cosine Transform 169 are essential for human recognition of faces. It is pos- 1.3. Overview of the Paper sible that global descriptions serve as a front-end for more detailed feature-based perception (Bruce, 1990). Following this introduction, Section 2 presents the mathematical definition of the discrete cosine trans- 1.2. Approach and Motivation form as well as its relationship to the KLT. Then, Section 3 discusses the basics of a face recognition sys- This paper investigates an alternative holistic method tem using the discrete cosine transform. It details the for face recognition and compares it to the popular KLT proposed algorithm and discusses the various param- approach. The basic idea is to use the discrete cosine eters that may affect its performance. It also explains transform (DCT) as a means of feature extraction for the pre-processing steps involved prior to the use of later face classification. The DCT is computed for a the DCT in recognition in order to improve its per- cropped version of an input image containing a face, formance. Section 4 highlights the proposed system’s and only a small subset of the coefficients is maintained performance based on experimental results. Finally, the as a feature vector. This feature vector may be con- paper ends with conclusions and suggestions for future ceived of as representing a point in a high-dimensional work.
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