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.

MCE-BASED FACE RECOGNITION

2001 ◽  
Vol 15 (08) ◽  
pp. 1311-1327 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
SHAO-HUA DENG ◽  
HONG-YUAN LIAO
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Classification Error ◽  
Complex Scenes ◽  
Minimum Classification Error ◽  
Complete Procedure ◽  
Human Faces

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

scholarly journals Face Detection with a portable architecture in Real Time

2019 ◽  
Author(s):  
Francesco De Feo ◽  
Pasquale De Luca
Keyword(s):  
Face Detection ◽  
New Technologies ◽  
Detection Algorithm ◽  
Live Streaming ◽  
Late 20Th Century ◽  
Video Cameras ◽  
Cascade Classifiers ◽  
Human Faces ◽  
Portable Architecture ◽  
Operating Methods

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

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

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°.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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.

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