A minimum classification error method for face recognition

Chen, L. H.; Chen, J. R.; Liang, Danning; Deng, Siyang; Liao, Hui

doi:10.1049/cp:19990399

Cited by 3 publications

(3 citation statements)

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“…It has been widely applied to several classifier structures, such as Multi-Layer Perceptrons [9], and Hidden Markov Models [7]. The essential aspect of this approach is to train the classifier structure so as to minimize the classification error rate using a gradient-based method together.…”

Section: Minimum Classification or Discriminative Trainingmentioning

confidence: 99%

Hidden Markov Models for Sensor Fusion of EMI and GRP

Gader¹

2003

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Paul Gader, Principal InvestigatorStatement of problem studied. An investigation of hidden Markov models (HMMs) as a means of processing Ground Penetrating Radar signals for discrimination between anti-tank (AT) and antipersonnel (AP) landmines and discrete clutter objects was conducted. Hidden Markov models had been used successfully previously in AT landmine detection with GPR but had not been used as a discriminant between mines and clutter nor had they been used on AP mines. Experiments were conducted on data collected at the JUXOCO calibration grid. Summary of the most important results.Continuous and discrete HMMs were trained and tested and evaluated on the grid using both the Baum-Welch and discriminative training algorithms. Experimental results suggest that discriminative training algorithms should be used for training HMMs for landmine detection, fusion of continuous and discrete models provided improved performance, and that, although HMMs with the existing feature sets perform well for detection of AT mines, new feature sets should be developed for discriminating between mines and clutter objects.

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Section: Minimum Classification or Discriminative Trainingmentioning

confidence: 99%

Hidden Markov Models for Sensor Fusion of EMI and GRP

Gader¹

2003

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“…Experimental results yielded 96% accuracy for surveillance. Chen et al (1999) proposed a minimum classification error rate-based face recognition system. The minimum classification error formulation is incorporated into a neural network classifier called a multilayer perceptron.…”

Section: Previous Workmentioning

confidence: 99%

Adaptive LVQ Classifier for Invariant Face Recognition

Khuwaja

2003

Cybernetics and Systems

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Visual communication plays an important role in human communication and interaction. In order to interact socially, we must be able to process faces in a variety of ways. This paper addresses the issue of recognition using a compact architecture of a learning vector quantization classifier that learns the correlation of patterns and identifies human faces. The suggested neural network structure is shown to exhibit robustness in achieving better classification results with both good generalization performance and a fast training time on a variety of test problems using standard and variable databases. Sources of variability include facial expression, gender, individual appearance, tilt, lighting conditions, and occluding objects (hair, spectacles, etc). Empirical results yield a peak accuracy rate of 100% (99.63% average) on the face recognition task for a random test set of 90 face images of 30 subjects from the Kuwait University databases on the network that is trained with another set of 60 faces of the same subjects. The computer execution time is about 0.04 s per face image on a Pentium II, 233 MHz, 32 MB, and Win98 PC.

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“…In Chen et al [34], a minimum classification error rate based face recognition system is proposed. The minimum classification error formulation is incorporated into a neural network classifier called a multilayer perceptron.…”

Section: Holistic Approaches For Face Recognitionmentioning

confidence: 99%

An Adaptive Combined Classifier System for Invariant Face Recognition

Khuwaja

2002

Digital Signal Processing

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A minimum classification error method for face recognition

Cited by 3 publications

References 0 publications

Hidden Markov Models for Sensor Fusion of EMI and GRP

Hidden Markov Models for Sensor Fusion of EMI and GRP

Adaptive LVQ Classifier for Invariant Face Recognition

An Adaptive Combined Classifier System for Invariant Face Recognition

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