Hyohoon Choi

Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances

We introduce a novel multimodal framework for face recognition based on local attributes calculated from range and portrait image pairs. Gabor coefficients are computed at automatically detected landmark locations and combined with powerful anthropometric features defined in the form of geodesic and Euclidean distances between pairs of fiducial points. We make the pragmatic assumption that the 2-D and 3-D data is acquired passively (e.g., via stereo ranging) with perfect registration between the portrait data and the range data. Statistical learning approaches are evaluated independently to reduce the dimensionality of the 2-D and 3-D Gabor coefficients and the anthropometric distances. Three parallel face recognizers that result from applying the best performing statistical learning schemes are fused at the match score-level to construct a unified multimodal (2-D+3-D) face recognition system with boosted performance. Performance of the proposed algorithm is evaluated on a large public database of range and portrait image pairs and found to perform quite well.

Automated Facial Feature Detection from Portrait and Range Images

We propose a novel technique to detect feature points from portrait and range representations of the face. In this technique, the appearance of each feature point is encoded using a set of Gabor wavelet responses extracted at multiple orientations and spatial frequencies. A vector of Gabor coefficients, called a jet, is computed at each pixel in the search window on a fiducial and compared with a set of jets, called a bunch, collected from a set of training data on the same type of fiducial. The desired feature point is located at the pixel whose jet is the most similar to the training bunch. This is the first time that Gabor wavelet responses were used to detect facial landmarks from range images. This method was tested on 1146 pairs of range and portrait images and high detection accuracies are achieved using a small number of training images. It is shown that co-localization using Gabor jets on range and portrait images resulted in better accuracy than using any single image modality. The obtained accuracies are competitive to that of other techniques in the literature.

Joint segmentation and classification of M-FISH chromosome images

Automatic segmentation and classification of M-FISH chromosome images are jointly performed using a six-feature, 25-class maximum-likelihood classifier. Preprocessing of the images including background correction and six-channel color compensation method are introduced. A feature transformation method, spherical coordinate transformation, is introduced. High correct classification results are obtained.

Automated facial feature detection and face recognition using Gabor features on range and portrait images

In this paper, we present a novel identity verification system based on Gabor features extracted from range (3D) representations of faces. Multiple landmarks (fiducials) on a face are automatically detected using these Gabor features. Once the landmarks are identified, the Gabor features on all fiducials of a face are concatenated to form a feature vector for that particular face. Linear discriminant analysis (LDA) is used to reduce the dimensionality of the feature vector while maximizing the discrimination power. These novel features were tested on 1196 range images. The same features were also extracted from portrait images, and the accuracies of both modalities were compared. A superior verification accuracy was obtained using the range data, and a highly competitive accuracy to that of other techniques in the literature was also obtained for the portrait data.

Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth Curves

In this paper a new framework for personal identity verification using 3-D geometry of the face is introduced. Initially, 3-D facial surfaces are represented by curves extracted from facial surfaces (facial curves). Two alternative facial curves are examined in this research: iso-depth and iso-geodesic curves. Iso-depth curves are produced by intersecting a facial surface with parallel planes perpendicular to the direction of gaze, at different depths from the nose tip. An Iso-geodesic curve is defined to be the locus of all points on the facial surface having the same geodesic distance from a given facial landmark (e.g. the nose tip). Once the facial curves are extracted, their characteristics are encoded by several features like the shape descriptors or polar Euclidean distances from the origin (nose tip). The final step is to verify or disapprove requests from users claiming the identity of registered individuals (gallery members) by comparing their features using Euclidean distance classifier or support vector machine (SVM). The performance results of the identity verification experiments are reported and a comparison is made between the two alternative curve-based facial surface representations.

Color Compensation of Multicolor FISH Images

Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Due to the overlap of excitation and emission spectra and the broad sensitivity of image sensors, the obtained images contain crosstalk between the color channels. The crosstalk complicates both visual and automatic image analysis and may eventually affect the classification accuracy in M-FISH. The removal of crosstalk is possible by finding the color compensation matrix, which quantifies the color spillover between channels. However, there exists no simple method of finding the color compensation matrix from multichannel fluorescence images whose specimens are combinatorially hybridized. In this paper, we present a method of calculating the color compensation matrix for multichannel fluorescence images whose specimens are combinatorially stained.

Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Multicolor fluorescence in-situ hybridization (m-fish) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available m-fish systems exhibit misclassifications of multiple pixel regions that are often larger than the actual chromosomal rearrangement. This paper presents a novel unsupervised classification method based on fuzzy logic classification and a prior adjusted reclassification method. Utilizing the chromosome boundaries, the initial classification results improved significantly after the prior adjusted reclassification while keeping the translocations intact. This paper also presents a new segmentation method that combines both spectral and edge information. Ten m-fish images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average.

Feature Normalization via Expectation Maximization and Unsupervised Nonparametric Classification For M-FISH Chromosome Images

Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Often, the intensity distributions between different images are found to be considerably different and the difference becomes the source of misclassifications of the pixels. Improved pixel classification accuracy is the most important task to ensure the success of the M-FISH technique. In this paper, we introduce a new feature normalization method for M-FISH images that reduces the difference in the feature distributions among different images using the expectation maximization (EM) algorithm. We also introduce a new unsupervised, nonparametric classification method for M-FISH images. The performance of the classifier is as accurate as the maximum-likelihood classifier, whose accuracy also significantly improved after the EM normalization. We would expect that any classifier will likely produce an improved classification accuracy following the EM normalization. Since the developed classification method does not require training data, it is highly convenient when ground truth does not exist. A significant improvement was achieved on the pixel classification accuracy after the new feature normalization. Indeed, the overall pixel classification accuracy improved by 20% after EM normalization.

MAXIMUM-LIKELIHOOD DECOMPOSITION OF OVERLAPPING AND TOUCHING M-FISH CHROMOSOMES USING GEOMETRY, SIZE AND COLOR INFORMATION

Since the birth of chromosome analysis by the aid of computers, building a fully automated chromosome analysis system has been the ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been one of the major challenges especially due to overlapping and touching chromosomes. In this paper we present a novel decomposition method for overlapping and touching chromosomes in M-FISH images. To overcome the limited success of previous decomposition methods that use partial information about a chromosome cluster, we have incorporated more knowledge about the clusters into a maximum-likelihood frame work. The proposed method evaluates multiple hypotheses based on geometric information, pixel classification results, and chromosome sizes, and a hypothesis that has a maximum-likelihood is chosen as the best decomposition of a given cluster. About 90% of accuracy was obtained for two or three chromosome clusters, which consist about 95% of all clusters with two or more chromosomes

Three Dimensional Face Recognition using Wavelet Decomposition of Range Images

Interest in face recognition systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper we propose a novel technique to extract features from 3D face representations. In this technique, first the nose tip is automatically located on the range image, then the range data from a hexagonal region of interest around this landmark is decomposed using Barycentric wavelet kernels. The dimensionality of the extracted coefficients at each resolution level is reduced using principal component analysis (PCA). These new features are tested on 206 range images, and a high classification accuracy is achieved using a small number of features. The obtained accuracy is competitive to that of other techniques in literature.