Robust in-plane and out-of-plane face detection algorithm using frontal face detector and symmetry extension

2018 ◽  
Vol 78 ◽  
pp. 26-41 ◽  
Author(s):  
Yu-Hsuan Tsai ◽  
Yih-Cherng Lee ◽  
Jian-Jiun Ding ◽  
Ronald Y. Chang ◽  
Ming-Chen Hsu
Keyword(s):  
Face Detection ◽  
Detection Algorithm ◽  
Plane Face ◽  
Out Of Plane ◽  
Face Detector

Research and Design of Face Detection Based on OpenCV in CodeBlocks

2012 ◽  
Vol 532-533 ◽  
pp. 974-978
Author(s):  
Xue Cong Lv ◽  
Zheng Bing Zhang
Keyword(s):  
Face Detection ◽  
Full Range ◽  
Detection Algorithm ◽  
Imaging Processing ◽  
Side Face ◽  
Processing Software ◽  
Human Faces ◽  
Research And Design ◽  
And Training ◽  
Face Detector

To implement the problem that the side face detector is slow and its detection rate is low, in this paper, we choose the Adaboost face detection algorithm based on statistics. Then the characteristics of imaging processing software OpenCV and the principle and training flow of Adaboost face detector are introduced. Further, combination with the supplement Haar-like features improved, the full range of face detection based on OpenCV in CodeBlocks is achievement, thereby decreasing the loss of the human faces.

scholarly journals An analysis on out-of plane face detection among female student and illumination effects using Sift and Surf

2016 ◽  
Author(s):  
Mohd Safirin Karis ◽  
Nursabillilah Mohd Ali ◽  
Asmidar Mohd Basar ◽  
Hazriq Izzuan Jaafar ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Face Detection ◽  
Female Student ◽  
Plane Face ◽  
Out Of Plane

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

scholarly journals Pre filtering techniques for face recognition based on edge detection algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 213
Author(s):  
Sheela Rani ◽  
Vuyyuru Tejaswi ◽  
Bonthu Rohitha ◽  
Bhimavarapu Akhil
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Facial Features ◽  
Human Face ◽  
Video Frames ◽  
Similar Idea ◽  
The Individual ◽  
The Given ◽  
Interesting Area

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

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