Abstract
This Biometrics refers to automatic recognition of individuals based on their physiological and/or behavioral characteristics. The displacement of the centre of pressure (COP) is a measure that has been successfully employed in studies regarding the postural control. So why not using the postural control as a behavioral characteristic to recognize individuals? The purpose of this study is to recognize individuals and to classify them by their age, gender, height and weight using postural features. The recognition approaches: Linear Discriminant Analysis (LDA), Support Vectors Machine (SVM) and K Nearest Neighbors (KNN) are applied to these biometric application. The postural features using in this application are extracted from data from twenty five healthy participants (average age 31 ± 11 years) standing in orthostatic position on an electromagnetic platform. The features are divided into three categories: classical features (extracted directly from the signals), mPCA features (extracted from the components trembling and rambling issued from the mPCA decomposition) and wavelet features (extracted from the components detail signal level 3 and detail signal level 2 issued from the wavelet decomposition. The results show that the ten subjects chosen for the recognition application are identified by the LDA with the rate of 80.43%. The classification applications (according to age, gender, height and weight) are performed for the all participants and the best resulted classification rates ranged from 82.61% to 93.48%. Most of these best rates are performed by the SVM classifiers using the totality of the features.
Highlights
Being able to determine effectively and exactly the identity of an individual or to determine its physical category, according to age or gender or size or weight, has become a critical issue because, these days access, secure and monitoring is a matter of great importance
Classifiers used are linear discriminant analysis (LDA) and KNearest Neighbors (KNN) with k = 1
- A Classification according to age. - A classification by gender. - A classification according to weight. - A classification according to height. These classifications are applied by three classifiers: LDA, KNN and Support Vector Machine (SVM)
Summary
Being able to determine effectively and exactly the identity of an individual or to determine its physical category, according to age or gender or size or weight, has become a critical issue because, these days access, secure and monitoring is a matter of great importance. The two categories of biometric identifiers include physiological and behavioral characteristics. Physiological characteristics are related to the shape of the body, and include fingerprint, face recognition, DNA, palm print, hand geometry, iris recognition. It becomes a focus of several researches and tends to associate to high security technology but the low cost of biometric technologies has, for a long time hampered their development. The researches interesting to biometrics and classification use several recognition approaches such as linear discriminant analysis (LDA) used to face and iris multimodal biometric recognition [ 21] or only face recognition [20, 27]. The recognition approaches: KNearest Neighbors (KNN), Support Vector Machine (SVM), linear discriminant analysis (LDA) and Neural Networks (NN) are used to classify human postural and gestural movements [24]
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