NDLI: The LogitBoost Based on Joint Feature for Face Detection

Content Provider	IEEE Xplore Digital Library
Author	Shishi Duan Xiangyang Wang Wanggen Wan
Copyright Year	2013
Description	Author affiliation: Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China (Shishi Duan; Xiangyang Wang; Wanggen Wan)
Abstract	In this paper, joint Haar-like feature is used for detecting faces in images. Our method is based on the co-occurrence Haar-like features which can capture the structural characteristic of the face, make it possible to construct more effective weak classifier. As the Haar-like, the joint Haar-like feature can be calculated very effective and has the robustness to addition of noise and change in illumination. The face detector is learned by stage wise selection which is different with Viola and Jones Detector is that we use LogitBoost. We perform two experiments: In the Experiment 1, we show that the LogitBoost [9] obtain higher performance than AdaBoost [2] [4] [11]. We have confirmed that our method based on LogitBoost yielded higher performance than AdaBoost. In the Experiment 2, the performance increase according with the num of the combined features. However, the time spent increase at huge growth rate, too. Therefore, we will research that how we can choose the optimal F at the acceptable training time in the latter work.
Starting Page	483
Ending Page	488
File Size	908392
Page Count	6
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Stage Wise Selection Detectors Discriminative Feature Co-Occurrence Feature extraction Boosting joint Haar-like feature Face Joints LogitBoost Receiver Operating Characteristic
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

FDA based fast haar-like feature selection for cascaded AdaBoost face detection

Multiclass object detection system in imaging sensor network using Haar-like features and Joint-Boosting algorithm

Real Time Face Detection System Using Adaboost and Haar-like Features

Multi-class object detection by part based approach

A classifier training method for face detection based on AdaBoost

Joint haar-like features for face detection

Research of usage of Haar-like features and AdaBoost algorithm in Viola-Jones method of object detection

An Enhanced Algorithm Of The Statistical Training Method In Boosting-Based Face Detection

AdaBoost Face Detection Based on Haar-Like Intensity Features and Multi-threshold Features

The LogitBoost Based on Joint Feature for Face Detection

Similar Documents

FDA based fast haar-like feature selection for cascaded AdaBoost face detection

Multiclass object detection system in imaging sensor network using Haar-like features and Joint-Boosting algorithm

Real Time Face Detection System Using Adaboost and Haar-like Features

Multi-class object detection by part based approach

A classifier training method for face detection based on AdaBoost

Joint haar-like features for face detection

Research of usage of Haar-like features and AdaBoost algorithm in Viola-Jones method of object detection

An Enhanced Algorithm Of The Statistical Training Method In Boosting-Based Face Detection

AdaBoost Face Detection Based on Haar-Like Intensity Features and Multi-threshold Features

The LogitBoost Based on Joint Feature for Face Detection