Lamarckian Genetic Algorithm in Neural Networking for Face Detection System

LAMARCKIAN GENETIC ALGORITHM IN NEURAL NETWORKING

LAMARCKIAN GENETIC ALGORITHM IN NEURAL NETWORKING
Face Detection System-based on Neural Network
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Table of Content
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1ReferencesReferences
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Introduction................................................................................................................................3
Background................................................................................................................................3
Discussion..................................................................................................................................4
Stage 1:...................................................................................................................................4
Stage 2:...................................................................................................................................7
Testing and Results....................................................................................................................7
Conclusion and Future Work...................................................................................................10
References................................................................................................................................12

2ReferencesReferences
Introduction
Training of a neural network for the task of detecting faces is considered to be a
challenging task, mostly due to the complexity and hardships during recognition of ‘non-
facial’ image prototypes. Face recognition simply implements discrimination of alike faces
among two classes, thus, spotting ‘facial images’ to ‘non-facial images’. Collecting
representative image samples (containing faces) is easy, however, it is hard to detect the ones
which does not have any. The training set’s size in the next class has been observed to grow
rapidly.
Thus, the following paper is aimed to present an algorithm based on neural network-
based system, which has the capability of detecting faces (front view) under gray-scale
imaging (Ronao & Cho 2016). The applied methods of training and algorithms are
generalized to be applied to recognition problems with respect to different faces, objects and
patterns, also. The following work is carried out, under specific avoidance of massive training
set usage; thus, the only selected images were added to the training set during the
progression. Moreover, the training methods are described (with network architecture) in the
Discussion section. Also, the system’s performance had also been examined, and results were
suitably eminent to signify the importance of the system (Results and Testing). Additionally,
the system is also described in an extended manner for better understanding. Lastly, the study
is concluded along with the future scope of further research.
Background
Facing recognition is based on the ground of image processing using any machinery
language, such as python, MATLAB and more. However, primarily the idea of image
processing is required to be understood to continue further with the study. Image processing
is defined as the procedure for conversion of an image file into a digitalized format, and also