Automatically extracting features for face classification using multi-objective genetic programming

© 2020 Owner/Author. This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.

Automatically extracting features for face classification using multi-objective genetic programming

10.26686/wgtn.13884935 ◽

2021 ◽

Author(s):

Ying Bi ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Feature Extraction ◽

Genetic Programming ◽

Classification Accuracy ◽

Multi Objective ◽

Face Classification ◽

A New Spacecraft Impact Damage Feature Extraction Algorithm Based on Dynamic Multi-Objective Optimization Method

2019 IEEE 58th Conference on Decision and Control (CDC) ◽

10.1109/cdc40024.2019.9029921 ◽

2019 ◽

Author(s):

Ting Xue ◽

Chun Yin ◽

Xuegang Huang ◽

Sara Dadras ◽

Yuhua Cheng ◽

...

Keyword(s):

Feature Extraction ◽

Impact Damage ◽

Optimization Method ◽

Multi Objective Optimization ◽

Multi Objective ◽

Analysis of the classification accuracy of a new MNF based feature extraction algorithm

10.1117/12.689961 ◽

2006 ◽

Cited By ~ 7

Author(s):

M. Greco ◽

M. Diani ◽

G. Corsini

Keyword(s):

Feature Extraction ◽

Classification Accuracy ◽

Paddy Seed Classification and Identifying Varieties using Random Assessment Classification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9879.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2682-2685

Keyword(s):

Image Analysis ◽

Feature Extraction ◽

Classification Accuracy ◽

Research Work ◽

Classification Algorithm ◽

Experimental Results ◽

Classification Algorithms ◽

Extraction Algorithm ◽

Knn Classification

The current research work focuses in developing an accurate and efficient classification and feature extraction algorithm for paddy seed image analysis. The paddy images that are preprocessed by applying hybrid mediangaustransform algorithms were segmented using Paddysegmatch algorithm. The resultant image’s features are extracted by applying the proposed enhanced rapid SURF feature extraction including various features of image. Later, the paddy seeds are classified to form different categories by applying the proposed Random Assessment Classification algorithm. Experimental results on Paddy seed realtime image analysis database show that the proposed method performs better classification accuracy compared with SVM and KNN classification algorithms.