Paired Facial Matching With Age and Gender Prediction | Report

Running head: PAIRED FACIAL MATCHING WITH AGE AND GENDER PREDICTION
Paired Facial Matching With Age and Gender Prediction
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Paired Facial Matching With Age and Gender Prediction2
Table of Contents
Abstract............................................................................................................................................3
Introduction......................................................................................................................................3
Research Questions..........................................................................................................................5
Research Methodology....................................................................................................................5
References........................................................................................................................................7

Paired Facial Matching With Age and Gender Prediction3
Abstract
Machine learning algorithms are used widely for various purposes. Machine learning algorithm
is considered to be a subpart of the Artificial intelligence. Simply it can be said that with the
recent evaluation of machine learning it has now become possible to predict future instances and
forecast data according to historical data. Machine learning models has the ability to learn from
historic data which helps to find hidden patterns and to make fruitful decisions with minimum
human interaction. Machine learning models are also known as predictive models.
One can detect age and gender much easily because with the increasing amount of social
platform and social media. Performing classification or detection is still significantly lacking
when performed on real world images which huge volume of data. Thus we can say
convolutional neural network is considered to be as a significant choice which can be
implemented using deep learning technique to detect age and gender together.
Introduction
For the analysis and prediction, age and gender are the two main facial attributes which
play a vital foundational place in the social media interaction through which the age and gender
can be identified using a single face image. It can be done using intelligent system or application
such as access control, human-computer interaction, law enforcement and marketing intelligence
(Antipov, Berrani and Dugelay, 2016). Moreover there were many works which earlier but faces
various issues and also the accuracy of the model did not turn up very well (Kingma & Adam,
2014).
Moreover it is said that many datasets use for such analysis doesn’t fit the benchmark for
age and gender detection (Eidinger, Enbar & Hassner, 2014). These type of images faces major
problems and challenges in the real world (Fu, Guo & Huang, 2010). Most of the times the
image may be too much blur in terms of low resolution then occlusions also the image may be
out-of-plane and many more (Golomb, Lawrence & Sejnowski, 1990). Thus the proposal is an
attempt to minimize the gap between the face recognition capabilities and those of age and
gender estimation methods (Jain & Learned-Miller, 2010).
We consider deep learning to be a subfield of machine learning where the algorithm is
used as the structure and the function of the brain which is basically called the artificial neural
network (Jia et al., 2014). Deep learning algorithm comprises multiple layers of different