scholarly journals Deep learning for face recognition on mobile devices

2020 ◽  
Vol 9 (3) ◽  
pp. 109-117 ◽  
Author(s):  
Belén Ríos‐Sánchez ◽  
David Costa‐da Silva ◽  
Natalia Martín‐Yuste ◽  
Carmen Sánchez‐Ávila

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.


2017 ◽  
Vol 2 (2) ◽  
pp. 31-35
Author(s):  
Akshada Abnave ◽  
Charulata Banait ◽  
Mrunalini Chopade ◽  
Supriya Godalkar ◽  
Soudamini Pawar ◽  
...  

M-learning or mobile learning is defined as learning through mobile apps, social interactions and online educational hubs via Internet or network using personal mobile devices such as tablets and smart phones. However, in such open environment examination security is most challenging task as students can exchange mobile devices or also can exchange information through network during examination. This paper aims to design secure examination management system for m- learning and provide appropriate mechanism for anti- impersonation to ensure examination security. The users are authenticated through OTP. To prevent students from exchanging mobile devices during examination, system re-authenticates students automatically through face recognition at random time without interrupting the test. The system also provides external click management i.e. prevent students from accessing online sites and already downloaded files during examination.


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