Gender Recognition using Convolutional Neural Network using MATLAB

 

Gender Recognition using Convolutional Neural Network

Abstract:

The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the  current  methodologies  used  in  pattern  recognition  applications.  By  combining  convolutional  and  subsampling  layers,  the  overall processing  layer  count  is  minimised  to  four.  Notably,  using  cross-correlation  versus  standard  convolution  tends  to alleviate  computing strain.  The  association  is  programmed  using  extended  worldwide  acquisition  frequencies  and  a  second-order  backpropagation  learning algorithmic framework. The demonstrated CNN approach has been examined using two freely downloadable facial statistics, SUMS and AT&T,  with  classification  accuracies  of  99.38%  and  98.75%,  respectively.  Furthermore,  the  neural  network's  algorithm  demonstrates exceptional efficiency by analysing and categorising a 32 by 32-pixel face picture in just 0.27 milliseconds, resulting in an outstanding consumption of over 3700 images per second. The successful performance of the proposed CNN methodology is further demonstrated by its speedy convergence throughout the training process, which requires less than 20 epochs. The results were produced to showcase the suggested CNN's outstanding accuracy in accurately categorising data, launching it as a realistic and effective real-time identification of the gender system.

 

Introduction

The  question  of  gender  categorization  first  surfaced  in psychophysical   research,   which   aimed   to   comprehend vision processing and discover distinctive traits that are used to  discriminate  between  male  and  female  subjects. Additional   investigations   have   demonstrated   that   it   is possible  to  use  the  differences  in  facial  masculinity  and femininity to improve the functionality of face recognition systems in several types of fields, such as computer vision, biometrics,  human-computer  interfaces,  and  surveillance. However,  real-world  circumstances  present  a  significant problem   in   managing   the   effects   of   lighting,   position changes, facial emotions, occlusions, backdrop distortions, and  noise  on  facial  pictures.  Therefore,  to  achievehigh, reasonable,  and  accurate  classification  performance,  these problems must be addressed in creating a substantial gender categorization system reliant on face analysis.

The   conventional   method   of   face   recognition,   which includes   gender   categorization   as   well,   involves   the sequential   steps   of   feature   extraction,   dimensionality reduction, image processing, and classification. An efficient feature   extractor   requires   previousapplication   domain knowledge  to  build.  It  might  be  difficult  to  ascertain  the optimal  mix  of  classifiers  to  achieve  high  classification accuracy  since  it  depends  on  the  method  employed  for feature extraction Furthermore, alterations to the issue domain may require a thorough   system   restructuring.   A   sequential   procedure comprising  picture  capture  and  processing,  dimensionality reduction, feature extraction, and classification is used in the classic  method  of  face  recognition,  which  includes  gender categorization. A prior understanding of the application area is required to create an efficient feature extractor. Selecting the  right  classifier  is  essential  since  it  affects  the  feature extraction technique used, and it can be difficult to identify which   combination   would  yield   the   best  classification accuracy.  Furthermore,  alterations  to  the  issue  domain frequently necessitate a total system overhaul.

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A  type  of  neural  network  that  combines  convolutional, subsampling,  and  densely  connected  layers  is  called  a convolutional   neural   network,   also   known   as   CNN. Compared to conventional methods, this network structure which  is  shown  in  Figure  1  offers  several advantages  in pattern recognition. Within a single network, the CNN can do  classification,  feature  extraction,  and  dimensionality reduction  all  at  once.  This  integrated  strategy  maintains efficiency     and     cost-effectiveness     while     improving recognition accuracy.

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