Investigación Secundaria

Several authors have demonstrated a number of techniques for face analysis, recogni- tion and applications [1][10][35][49][66][93][94][97][100][102], few to mention. Illumina- tion is one of the basic characteristics of a visible surface and it provides information for scene interpretation. Recent developments in this field have shown that there is room for improvements. Most of the traditional face recognition algorithms are satisfactory under controlled conditions, however, when dealing with performance degrading issues such as variation in pose, illumination, and facial expression, their accuracy is greatly diminished. As the performance of a face recognition technique is significantly changed under various illumination and lighting effects, illumination is known to be one of the key factors that plays an important role in human face recognition system design.

The approaches proposed for the problem of variable illumination in face recog- nition are mostly based on illumination modeling, preprocessing, and illumination invariant feature extraction. Face modeling techniques are basically statistical or physical models where linear subspaces with low dimensions form different illumina-

tion conditions to extract the model parameters [8][35][56]. The main limitation of this category is that the images need to construct a linear subspace. In [55], Lee et al. show that there exists configurations of single light source directions so that the obtained images of each object can be directly used as basis images of the linear subspaces effective for face recognition. Another method for face recognition under illumination variation is to simulate the distribution of the images with varying illu- minations and generate new training images for face recognition with single frontal views [113]. The approaches in this category may have the downside that several training sample images under varying illumination might be required for training.

The second category, which is known as preprocessing and normalization ap- proaches, is referred to as improving the illumination and lighting conditions in images where in most of the cases there is no need to face models or surface information. Histogram equalization (HE) [85] and gamma intensity correction (GIC) [85] are very well known techniques in this group. Quotient image (QI) [86] and self-quotient image (SQI) [100] are also considered as preprocessing ideas although they implicitly indi- cate illumination invariants. The quotient image approach eventually determines the ratio between a given test image and linear combination of three other images of the same face under different illumination effects. This ratio is assumed to be illumination invariant. The difference between QI and SQI is that the illumination invariant ratio for SQI is obtained using a given test image and its smoothed version. SQI, which is in fact a multiscale retinex approach, is simpler than QI as it needs a single image only. In [103], Xie and Lam proposed a preprocessing and illumination normalization algorithm for face recognition, in which the intensity of pixels is locally normalized using a small window centered at the corresponding pixel. For most of the methods in this category there is a compromise between the simplicity and performance.

For the illumination invariant based techniques, illumination invariant feature extraction is the main goal. For instance, Chen et al. [18] proposed a discrete cosine transform (DCT) based method where illumination variations are mainly assumed

to lie in the low-frequency subbands. Local binary pattern (LBP) [67] is another illumination invariant approach in which local contrast based image representation is employed to improve illumination losses. In [1], the authors address that several commonly used image representations, such as edge maps that are often assumed to be insensitive to illumination variations, are insufficient for the problem of illumination invariant face recognition.

Recently, several interesting techniques have been demonstrated for face recogni- tion that take the frequency information of images into consideration. Zhang et al. [110] proposed a discrete wavelet transform (DWT) based algorithm using denois- ing techniques to detect and eliminate the illumination effects. In [14], it has been shown that wavelet based NeighShrink denoising technique, employing two parame- ters λ1 and λ2, leads to higher recognition rates. El Aroussi et al. [30] introduced the use of steerable pyramid (S–P) to compute the statistics of each block by di- viding S–P subbands. Nabatchian et al. [66] showed that a simple lowpass filtering followed by a weighted voting scheme (WVS) can offer better results for illumination invariant recognition purposes. In [61], curvelet transform (CVT) was shown to be useful in recognizing changes in facial expression, however, there is no examples of images with significant variations in illumination such as the ones in the Extended- Yale B and CMU-PIE databases. Although some of the mentioned techniques do not address the problem of illumination invariant face recognition unless a preprocessing step is applied first, they emphasize the role of multiresolution analysis and frequency component filtering.

Multiscale representation of signals has been used in a number of image process- ing and computer vision applications from multiresolution image representation [48] to our recent work on multifocus image fusion and shape-from-focus [4]. In [13], optimization of filter banks is investigated for invariant supervised texture segmenta- tion. Multiscale directional filter bank is discussed extensively in [19] to suppress the aliasing effect as well as to minimize the reduction in frequency resolution where the

problem of aliasing in decimated bandpass images on directional decomposition has been addressed. Multiresolution analysis or multiscale approximation was basically referred to as the theory and design of discrete wavelet transform (DWT) and filter banks, and later on extended to multiwavelets, dual-tree complex wavelet transform (DT-C1_{WT), curvelets, higher-density discrete wavelet transform (HD-DWT), and} double-density dual-tree complex wavelet transform (DD-DTCWT). Multiresolution analysis allows the decomposition of a signal into its frequency components known as approximation and details [23][59][60]. It is basically an analysis-synthesis config- uration, where a signal is represented in various frequency subbands via the analysis side of a wavelet filter bank. In a perfect reconstruction filter bank the synthesis side in turn can reconstruct the original signal.

The double-density dual-tree complex wavelet transform is a powerful multiresolu- tion tool which is in fact a notable enhancement over the weakness of DWT in general. As the transformation increases the density (number) of the high-frequency subbands, and due to the fact that we need to separate and remove illumination, which mostly lies in the low-frequency part of images, the DD-DTCWT is a reasonable choice of algorithm to reduce the illumination effects in images.