Robust detection and recognition system based on facial extraction and decision tree

Abstract EN

Automatic face recognition system is suggested in this work on the basis of appearance based features focusing on the whole image as well as local based features focusing on critical face points like eyes, mouth, and nose for generating further details.

Face detection is the major phase in face recognition systems, certain method for face detection (Viola-Jones) has the ability to process images efficiently and achieve high rates of detection in real time systems.

Dimension reduction and feature extraction approaches are going to be utilized on the cropped image caused by detection.

One of the simple, yet effective ways for extracting image features is the Local Binary Pattern Histogram (LBPH), while the technique of Principal Component Analysis (PCA) was majorly utilized in pattern recognition.

Also, the technique of Linear Discriminant Analysis (LDA) utilized for overcoming PCA limitations was efficiently used in face recognition.

Furthermore, classification is going to be utilized following the feature extraction.

The utilized machine learning algorithms are PART and J48.

The suggested system is showing high accuracy for detection with Viola-Jones 98.75, whereas the features which are extracted by means of LDA with J48 provided the best results of (F-measure, Recall, and Automatic face recognition system is suggested in this work on the basis of appearance based features focusing on the whole image as well as local based features focusing on critical face points like eyes, mouth, and nose for generating further details.

Face detection is the major phase in face recognition systems, certain method for face detection (Viola-Jones) has the ability to process images efficiently and achieve high rates of detection in real time systems.

Dimension reduction and feature extraction approaches are going to be utilized on the cropped image caused by detection.

One of the simple, yet effective ways for extracting image features is the Local Binary Pattern Histogram (LBPH), while the technique of Principal Component Analysis (PCA) was majorly utilized in pattern recognition.

Also, the technique of Linear Discriminant Analysis (LDA) utilized for overcoming PCA limitations was efficiently used in face recognition.

Furthermore, classification is going to be utilized following the feature extraction.

The utilized machine learning algorithms are PART and J48.

The suggested system is showing high accuracy for detection with Viola-Jones 98.75, whereas the features which are extracted by means of LDA with J48 provided the best results of (F-measure, Recall, and Precision).