Authors :
Surabhi M; Shelja Jose M
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3I3VgVf
DOI :
https://doi.org/10.5281/zenodo.7647950
Abstract :
Face Attribute Prediction including Age,
Gender is an interesting topic among many researchers
and is one of the most interesting activities in pattern
recognition, personal computer interaction. Most
applications use this process because a person’s face is
considered a very rich source of information. This paper
not only predict Age and Gender but also predicts
Emotions and Racism from a featured image using a deep
face method. Dataset used for training the system is FER
(face emotion recognition) from kaggle. Fer2013 contains
approximately 30,000 facial RGB images of different
expressions with size restricted to 48×48 and the main
labels of it can be divided into 7 types of classes namely
angry, disgusted, fearful, happy, neutral, sad, and
surprised.
For testing IMDB and Wikipedia data set are used.
IMDB WIKI data set is the largest publicly available
facial data with gender, age, and name. IMDB contains
460,723 images and Wikipedia contains 62,328 images.
The overall accuracy achieved by the model is 97.23
percentage which was considerably high as compared to
the previous models.
Face Attribute Prediction including Age,
Gender is an interesting topic among many researchers
and is one of the most interesting activities in pattern
recognition, personal computer interaction. Most
applications use this process because a person’s face is
considered a very rich source of information. This paper
not only predict Age and Gender but also predicts
Emotions and Racism from a featured image using a deep
face method. Dataset used for training the system is FER
(face emotion recognition) from kaggle. Fer2013 contains
approximately 30,000 facial RGB images of different
expressions with size restricted to 48×48 and the main
labels of it can be divided into 7 types of classes namely
angry, disgusted, fearful, happy, neutral, sad, and
surprised.
For testing IMDB and Wikipedia data set are used.
IMDB WIKI data set is the largest publicly available
facial data with gender, age, and name. IMDB contains
460,723 images and Wikipedia contains 62,328 images.
The overall accuracy achieved by the model is 97.23
percentage which was considerably high as compared to
the previous models.
