Multi-View Face Database Recognition Using Phase Congruency and SVM Classifier

Huang, Zhi-Kai; Liu, Dehui; Zhang, Weizhong; Hou, Ling-Ying

doi:10.1109/iccee.2008.101

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…TELKOMNIKA Telecommun Comput El Control − Euclidean distance The ED is the straight-line distance between two points in Euclidean space, with this distance, Euclidean space becomes a metric space [35]. (16) − Manhattan distance…”

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confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing a low-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associated angles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation, three competitive classifiers: Euclidean distance (ED), cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.

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mentioning

confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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Illumination invariant face recognition based on the new phase features

Zhang

Pan

Tang

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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Improving phase congruency for EEG data reduction

Logesparan

Rodriguez‐Villegas

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Abstract-Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90% of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state-of-the-art denoised phase congruency-which calculates a constant noise threshold-applied to the same dataset.

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Multi-View Face Database Recognition Using Phase Congruency and SVM Classifier

Cited by 4 publications

References 8 publications

Towards better performance: phase congruency based face recognition

Towards better performance: phase congruency based face recognition

Illumination invariant face recognition based on the new phase features

Improving phase congruency for EEG data reduction

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