3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals

Ocegueda, Omar; Fang, Tianhong; Shah, Shishir K.; Kakadiaris, Ioannis A.

doi:10.1109/tpami.2012.126

Cited by 41 publications

(11 citation statements)

References 23 publications

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“…Their results indicated that it is possible to recognize race information with only 3D mesh of human face. Similar results have been reported by Ocegueda et al [144], who also investigated finding the most discriminative regions of the face for 3D racial face classification. Using Gauss-Markov posterior marginals (GMPM) for computing discriminative map of subjects from BU-3DFE database (Asian/White), they performed cross validation on FRGC v2.0 for the aim of comparing with Toderci's work.…”

Section: D Facessupporting

confidence: 89%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

156

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Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.

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Section: D Facessupporting

confidence: 89%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

156

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“…These 40 wavelet packets were selected using Simulated Annealing [21]. Alternatively, Omar et al [22] developed a method to weight different parts of the face based on their discriminability. To forego such an expensive search, we propose a variation of LC-KSVD that learns a set of dictionary atoms which perform well in recognition for the training set.…”

Section: )mentioning

confidence: 99%

3D face recognition for partial data using Semi-Coupled Dictionary Learning

Chu

Shah

Kakadiaris

2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semicoupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-ofthe-art wavelet signatures used for 3D face recognition.

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“…This assumption is specified by considering a Markovian priori distribution. Gauss Markov Measure Field (GMMF) [4] is one of the models that combines Bayesian estimation with Markov Random Field and it is used in many classification tasks, [5,6,7,8,10,11,12,13]. One of the main difficulties for GMMF, as for all methods based on the combination of Bayesian estimation and MRF for image segmentation, is the likelihood computation.…”

Section: Introductionmentioning

confidence: 99%

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

Dalmau

Alarcón

Oliva

2017

Sensors

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Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification.

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3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals

Cited by 41 publications

References 23 publications

Learning Race from Face: A Survey

Learning Race from Face: A Survey

3D face recognition for partial data using Semi-Coupled Dictionary Learning

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

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