Deep Neural Network for Better Face Processing
P.Anjaneyulu1, S.V.S Prasad2, V.Syambabu3, A.Vamshi Kumar4
1P.Anjaneyulu, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
2S.V.S Prasad, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
3V.Syambabu, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
4A.Vamshi Kumar, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2236-2239 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6238098319/2019©BEIESP | DOI: 10.35940/ijrte.C6238.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.
Keywords: Raspberry Pi, OpenCV, Deep Neural Network, Deep learning, Camera Sensor.
Scope of the Article: Deep Learning.