Journal of Software Engineering & Software Testing ISSN: 2457-0516 (Online)

Author: Md. Rakibul Hasan

Abstract: Background: Computer engineering education has called for a shift from the acquisition of Artificial Intelligence (AI) knowledge to the development of professional skills.

 

Objectives: The study aims to assess the effectiveness of using different Teaching-Learning Methods and Media in the improvement in each of the Cognitive, and Affective Skills among computer science students.

 

Material and Methods: A study was conducted among 11th-semester B.Sc. and 1st-semester M.Sc. students in the Department of Computer Science and Engineering (CSE) Islamic University, Kushtia, Bangladesh. It was before and after comparison non-randomized study. The test was conducted after teaching by the traditional methods and after teaching using Blend of Different Teaching-Learning Methods and Media - a combination of lecture with FERET videos, photograph, exercises, role play and group discussions of an accompanying Microsoft PowerPoint slideshow. Marks obtained were recorded, and analysis of data was done.

 

Results: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses the personal characteristics of a person to identify a person's identity. The human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognizes a face as individuals. The stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition patterns namely, the Eigenface method and the Fisherface method.

 

Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigenfaces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image.