Curvelet and ridgelet based multimodal biometric recognition system using weighted similarity approach

Arivazhagan, S.; Sekar, J. Raja; Priyadharshini, S.

doi:10.14429/dsj.64.3469

Cited by 2 publications

(1 citation statement)

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“…Physical factors includes the pose, expression etc., External factors include ageing factor, scale, occlusion etc., [30]. The image sequences from the videos are active in several research areas such as surveillance, biometric, computer and embedded application field for face recognition [12,32]. The analysis of Face recognition is depends on classifying and locating the face and non-face in images irrespective of size, position, and reflection condition [5].…”

Section: Introductionmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

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In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

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Section: Introductionmentioning

confidence: 99%