Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

• Autorzy: Sable Archana Harsing , Talbar Sanjay N.

Identyfikatory

DOI

10.1515/bams-2018-0033

• Języki publikacji: EN

Abstrakty

EN

Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

Słowa kluczowe

EN

adaptive GLOH; face recognition; neural network; plastic surgery; PSO

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 1
• Strony: art. no. 20180033
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Sable Archana Harsing

• Swami Ramanand Teerth Marathwada University, School of Computational Sciences, Vishnupuri Nanded, India

autor

Talbar Sanjay N.

• Department of Electronics and Telecommunication Engineering, Shri Guru Gobingji Singh Institute of Engineering and Technology, Nanded, Maharashtra, India

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).