In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Audio- and video-based biometric person authentication

The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented. Description and limitations of face databases which are used to test the performance of these face recognition algorithms are given. A brief summary of the face recognition vendor test (FRVT) 2002, a large scale evaluation of automatic face recognition technology, and its conclusions are also given. Finally, we give a summary of the research results. Keywords—Combined classifiers, face recognition, graph matching, neural networks.

/pdf/face-recognition-a-literature-review-3slmtnj2yk.pdf

Face Recognition: A Literature Review

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.

/pdf/anthropometric-3d-face-recognition-54hb2lb85n.pdf

Anthropometric 3D Face Recognition

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 (eg 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

/pdf/three-dimensional-face-recognition-using-iso-geodesic-and-4qkvxeqfi2.pdf

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

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.

/pdf/passive-multimodal-2-d-3-d-face-recognition-using-gabor-2j9suj5o4g.pdf

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

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

/pdf/automated-facial-feature-detection-and-face-recognition-3rz6sealf8.pdf

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

/pdf/segmentation-and-fuzzy-logic-classification-of-m-fish-5anokoczch.pdf

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

/pdf/automated-facial-feature-detection-from-portrait-and-range-1kzrm3z0e0.pdf

Hyohoon Choi

Papers

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

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

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

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

Automated Facial Feature Detection from Portrait and Range Images