Optimized Parallel Implementation of Face Detection Based on Embedded Heterogeneous Many-Core Architecture

2017 ◽  
Vol 31 (07) ◽  
pp. 1756011 ◽  
Author(s):  
Fang Gao ◽  
Zhangqin Huang ◽  
Shulong Wang ◽  
Xinrong Ji
Keyword(s):  
High Resolution ◽  
Face Detection ◽  
Parallel Implementation ◽  
Detection System ◽  
Local Binary Patterns ◽  
Detection Algorithm ◽  
Computing Platform ◽  
Computing Performance ◽  
Many Core ◽  
Dual Core

Computing performance is one of the key problems in embedded systems for high-resolution face detection applications. To improve the computing performance of embedded high-resolution face detection systems, a novel parallel implementation of embedded face detection system was established based on a low power CPU-Accelerator heterogeneous many-core architecture. First, a basic CPU version of face detection prototype was implemented based on the cascade classifier and Local Binary Patterns operator. Second, the prototype was extended to a specified embedded parallel computing platform that is called Parallella and consists of Xilinx Zynq and Adapteva Epiphany. Third, the face detection algorithm was optimized to adapt to the Parallella architecture to improve the detection speed and the utilization of computing resources. Finally, a face detection experiment was conducted to evaluate the computing performance of the proposal in this paper. The experimental results show that the proposed implementation obtained a very consistent accuracy as that of the dual-core ARM, and achieved 7.8 times speedup than that of the dual-core ARM. Experiment results prove that the proposed implementation has significant advantages on computing performance.

Face detection system design for real time high resolution smart camera

2009 ◽  
Author(s):  
Yasir M. Mustafah ◽  
Abbas Bigdeli ◽  
Amelia W. Azman ◽  
Brian C. Lovell
Keyword(s):  
High Resolution ◽  
System Design ◽  
Real Time ◽  
Face Detection ◽  
Detection System ◽  
Smart Camera

Efficient Half Face Detection System Based on Linear Binary Pattern

2020 ◽  
Vol 17 (5) ◽  
pp. 2342-2348
Author(s):  
Ashutosh Upadhyay ◽  
S. Vijayalakshmi
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Data Set ◽  
Computation Cost ◽  
Face Features ◽  
Positive Rate ◽  
The Face

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

2014 ◽  
Vol 490-491 ◽  
pp. 1259-1266 ◽  
Author(s):  
Muralindran Mariappan ◽  
Manimehala Nadarajan ◽  
Rosalyn R. Porle ◽  
Vigneswaran Ramu ◽  
Brendan Khoo Teng Thiam
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Biometric Technology ◽  
Main Challenge ◽  
Human Face Detection ◽  
Color Detection ◽  
Large Face

Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

scholarly journals Pest Animal's Detection, and Habitat Identification in Low-resolution Airborne Thermal Imagery

2020 ◽  
Author(s):  
Anwaar Ulhaq
Keyword(s):  
Invasive Species ◽  
High Resolution ◽  
Object Detection ◽  
Visual Analysis ◽  
Detection System ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Small Object ◽  
Pest Species ◽  
Thermal Imagery

Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

A Realization of Face Detection System Based on ARM Linux

2013 ◽  
Vol 333-335 ◽  
pp. 864-867 ◽  
Author(s):  
Cong Ting Zhao ◽  
Hong Yun Wang ◽  
Jia Wei Li ◽  
Zi Lu Ying
Keyword(s):  
Embedded System ◽  
Monitoring System ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Monitoring ◽  
Adaboost Algorithm ◽  
Arm Processor ◽  
The Face ◽  
Detection Effect

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

The Viola-Jones Face Detection Algorithm Analysis: A Survey

2021 ◽  
pp. 85-95
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Post Processing ◽  
Integral Image ◽  
Robust Detection ◽  
Processing Step ◽  
Haar Feature

In this paper, we analysis the Viola-Jones algorithm, the most real-time face detection system has been used. It is consisting from three main concepts to enable a robust detection: the integral image for Haar feature computation, Adaboost for selecting feature and cascade to make resource allocation more efficient. Here we propose each stage starting from Integral image to the end with Cascading and some of algorithmic description for stages. The Viola-Jones algorithm gives multiple detections, a post-processing step which reduce detection redundancy using Adaboost and cascading.

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