A new method for combined face detection and identification using interest point descriptors

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification Â which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting. Â Â Thus the proposed technique Â Â is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.Â Â It is very useful in identify a single person from a group of faces. Â Â Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. Â Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

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The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220960247 ◽

2020 ◽

pp. 014233122096024

Author(s):

Yanjie Liang ◽

Zhiyong Gao ◽

Jianmin Gao ◽

Guangnan Xu ◽

Rongxi Wang

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Principal Component ◽

Process Industry ◽

New Method ◽

Kernel Principal Component Analysis ◽

Fault Identification ◽

Coupling Analysis ◽

Detection And Identification ◽

Fault Detection And Identification

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

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A new method for face detection in colour images for emotional bio-robots

Science China Technological Sciences ◽

10.1007/s11431-010-4132-z ◽

2010 ◽

Vol 53 (11) ◽

pp. 2983-2988 ◽

Cited By ~ 5

Author(s):

Xu Zhang ◽

ShuJun Zhang ◽

Kevin Hapeshi

Keyword(s):

Face Detection ◽

New Method

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