Volume 20 No 12 (2022)
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Hybrid Features-Based Multimodal Biometric Identification using Random Forest Classifier and Convolutional Neural Network
Khaja Ziauddin, Dr. Vikas Somani
In many areas where personal identification is important, security is of great importance. Biometric or multi-biometric systems, which include the physiological and behavioral features of individuals, are more preferred because traditional methods are insufficient and cannot provide security. In the study, a new approach of multimodal biometric identification is proposed consisting of the fingerprint and finger knuckle print (FKP). A deep convolutional neural network (CNN) based hybrid feature extraction technique is utilized along with the GLCM (Gray Level Co-occurrence Matrix) and wavelet moments. Classification of extracted features is achieved by using random forest classifier with simulation results in terms of F-Score, accuracy, sensitivity,precision, and specificity
CNN, FKP, GLCM, Random Forest Classifier, Wavelet Moments
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