Dual Shot Face Detecting using Deep Learning
Nishath Ansari1, Suresh Dara2, Jaala Shruthi3
1Nishath Ansari, Computer Science Department, B.V. Raju institute of technology, Narsapur, Telangana, India.
2Suresh Dara, Computer Sscience Department, B.V. Raju institute of technology, Narsapur, Telangana, India.
3Jaala Shruthi, Computer Science Department, B.V. Raju institute of technology, Narsapur, Telangana, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5669-5672 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9930038620/2020©BEIESP | DOI: 10.35940/ijrte.F9930.038620
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In the paper, we have used a deep learning technique to identify dual faces i.e. nothing but detecting dual shot faces. As the data is emerging day by day with high dimensionality, recognizing dual faces is a major problem. So wasting time on identifying images is like fiddling around. In order to save time and get absolute accuracy we have implemented a fast preprocessing technique named as Convolutional Neural Network (CNN) along with feature extraction technique which is used to knob the relevant features to detect and identify images/faces. By performing this robust method, our intention is to detect dual images in an efficient way. This technique results in decreased feature cardinality and preserves unique efficiency of the data. The experiment is performed on extensive well liked face detecting benchmark datasets, Wider Face and FDDB. CNN with FE demonstrates the results with superiority and the accuracy was in-depth analyzed by CNN classifier.
Keywords: Deep Learning, CNN, Feature extraction, face detection, feature subset and Dual Shot.
Scope of the Article: Deep Learning.