Face recognition using Gabor filters, PCA and neural networks

Slavković, Marijeta; Reljin, Branimir; Gavrovska, Ana; Milivojević, Milan S.

doi:10.1109/iwssip.2013.6623443

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Over the years, these models have performed incredibly well in many object perception tasks and today are recognized as equal alternatives to statistical techniques. In face perception, biologically-inspired methodologies have been applied successfully for some years and have proven reliable as well as accurate (Lyons et al, 1998; Wang and Chua, 2005; Perlibakas, 2006; Rose, 2006; Meyers and Wolf, 2008; Pisharady and Martin, 2012; Li et al, 2013; Slavkovic et al, 2013; Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Presentation Attack Detection for Face Biometrics

Tsitiridis

Conde

Ayllon

et al. 2019

Front. Comput. Neurosci.

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Today, face biometric systems are becoming widely accepted as a standard method for identity authentication in many security settings. For example, their deployment in automated border control gates plays a crucial role in accurate document authentication and reduced traveler flow rates in congested border zones. The proliferation of such systems is further spurred by the advent of portable devices. On the one hand, modern smartphone and tablet cameras have in-built user authentication applications while on the other hand, their displays are being consistently exploited for face spoofing. Similar to biometric systems of other physiological biometric identifiers, face biometric systems have their own unique set of potential vulnerabilities. In this work, these vulnerabilities (presentation attacks) are being explored via a biologically-inspired presentation attack detection model which is termed “BIOPAD.” Our model employs Gabor features in a feedforward hierarchical structure of layers that progressively process and train from visual information of people's faces, along with their presentation attacks, in the visible and near-infrared spectral regions. BIOPAD's performance is directly compared with other popular biologically-inspired layered models such as the “Hierarchical Model And X” (HMAX) that applies similar handcrafted features, and Convolutional Neural Networks (CNN) that discover low-level features through stochastic descent training. BIOPAD shows superior performance to both HMAX and CNN in all of the three presentation attack databases examined and these results were consistent in two different classifiers (Support Vector Machine and k -nearest neighbor). In certain cases, our findings have shown that BIOPAD can produce authentication rates with 99% accuracy. Finally, we further introduce a new presentation attack database with visible and near-infrared information for direct comparisons. Overall, BIOPAD's operation, which is to fuse information from different spectral bands at both feature and score levels for the purpose of face presentation attack detection, has never been attempted before with a biologically-inspired algorithm. Obtained detection rates are promising and confirm that near-infrared visual information significantly assists in overcoming presentation attacks.

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Section: Introductionmentioning

confidence: 99%

Bio-Inspired Presentation Attack Detection for Face Biometrics

Tsitiridis

Conde

Ayllon

et al. 2019

Front. Comput. Neurosci.

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Feature Extraction-Selection Scheme for Hyperspectral Image Classification Using Fourier Transform and Jeffries-Matusita Distance

Salgado

Ponomaryov

2015

Advances in Artificial Intelligence and Its Applications

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Face Recognition Using 2DPCA and ANFIS Classifier

Shah

Kher

Patel

2014

Advances in Intelligent Systems and Computing

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A biometric system provides automatic identification of a character based on a unique feature or attribute possessed by the creature. Most practical iris recognition systems use original algorithms developed by Daugman and these algorithms are competent to create perfect recognition rates but not includes time requirement in account and extremely suffered when pupil is illuminated with light intensity. For the efficient pupil detection a morphological bridged canny edge detection technique is proposed, which is especially designed to handle pupil occlusion problem. For the feature selection and mining part, most conventional iris recognition systems relies on wavelet features extracted from rubber sheet model, but the wavelet feature extraction is a complex and time consuming process, to handle this problem in this paper rubber sheet model of iris part is exclusively used as feature. The third and most crucial modification proposed in this paper, is in the feature classification part, after feature space formation using rubber sheet model, ANFIS classifier is used for recognition efficiency.

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Face recognition using Gabor filters, PCA and neural networks

Cited by 5 publications

References 8 publications

Bio-Inspired Presentation Attack Detection for Face Biometrics

Bio-Inspired Presentation Attack Detection for Face Biometrics

Feature Extraction-Selection Scheme for Hyperspectral Image Classification Using Fourier Transform and Jeffries-Matusita Distance

Face Recognition Using 2DPCA and ANFIS Classifier

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