title: Kruskal-Wallis-based computationally efficient feature selection for face recognition.
abstract: Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.
authors: Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan ; Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad, Islamabad 44000, Pakistan.
authors: Sajid
authors: 0000-0003-0270-2869
authors: S
authors: Ali Khan
authors: Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan.
authors: Ayyaz
authors: A
authors: Hussain
authors: Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan.
authors: Abdul
authors: A
authors: Basit
authors: Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan.
authors: Sheeraz
authors: 0000-0003-2321-3845
authors: S
authors: Akram
mesh_terms: D001185:Artificial Intelligence; D005145:Face; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results