Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

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 personal characteristics of a  person to identify the person's identity. Human face recognition procedure basically consists&nbs

Project Title

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Project Area of Specialization

Cyber Security

Project Summary

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 personal characteristics of a 
person to identify the person's identity. 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 recognize a face as individuals. 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 pattern namely, Eigenface method and 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 Eigen faces was formed (face space) by 
finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of 
this project face detection system with face recognition is Image processing. The software 
requirements for this project is matlab software.
Keywords: face detection, Eigen face, PCA
Extension: There are vast number of applications from this face detection project, this project 
can be extended that the various parts in the face can be detect which are in various directions

Project Objectives

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Project Implementation Method

Reconstruction cannot be used as a means of face detection in images in near real-time 
since it would involve resizing the face detection window area and large matrix multiplication, 
both of which are computationally expensive. However, reconstruction can be used to verify 
whether potential face locations identified by the deformable template algorithm actually contain a 
face. If the reconstructed image differs greatly from the face detection window then the window
probably does not contain a face. Instead of just identifying a single potential face location, the 
face detection algorithm can be modified to output many high 'faceness' locations which can be 
verified using reconstruction. This is especially useful because occasionally the best 'faceness' 
location found by the deformable template algorithm may not contain the ideal frontal view face 
pixel area.

Benefits of the Project

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Technical Details of Final Deliverable

The computational models, which were implemented in this project, were chosen after 
extensive research, and the successful testing results confirm that the choices made by the 
researcher were reliable.The system with manual face detection and automatic face recognition did 
not have a recognition accuracy over 90%, due to the limited number of eigenfaces that were used 
for the PCA transform. This system was tested under very robust conditions in this experimental 
study and it is envisaged that real-world performance will be far more accurate.The fully 
automated frontal view face detection system displayed virtually perfect accuracy and in the 
researcher's opinion further work need not be conducted in this area.
The fully automated face detection and recognition system was not robust enough to 
achieve a high recognition accuracy. The only reason for this was the face recognition subsystem 
did not display even a slight degree of invariance to scale, rotation or shift errors of the segmented 
face image. This was one of the system requirements identified in section 2.3. However, if some 
sort of further processing, such as an eye detection technique, was implemented to further 
normalise the segmented face image, performance will increase to levels comparable to the 
manual face detection and recognition system. Implementing an eye detection technique would be 
a minor extension to the implemented system and would not require a great deal of additional 
research.All other implemented systems displayed commendable results and reflect well on the 
deformable template and Principal Component Analysis strategies.The most suitable real-world 
applications for face detection and recognition systems are for mugshot matching and surveillance. 
There are better techniques such as iris or retina recognition and face recognition using the thermal 
spectrum for user access and user verification applications since these need a very high degree of 
accuracy.The real-time automated pose invariant face detection and recognition system proposed 
in chapter seven would be ideal for crowd surveillance applications. If such a system were widely 
implemented its potential for locating and tracking suspects for law enforcement agencies is 
immense.
The implemented fully automated face detection and recognition system (with an eye 
detection system) could be used for simple surveillance applications such as ATM user security, 
while the implemented manual face detection and automated recognition system is ideal of 
mugshot matching. Since controlled conditions are present when mugshots are gathered, the 
frontal view face recognition scheme should display a recognition accuracy far better than the 
results, which were obtained in this study, which was conducted under adverse conditions.

Final Deliverable of the Project

Hardware System

Core Industry

IT

Other Industries

Core Technology

Others

Other Technologies

Sustainable Development Goals

Partnerships to achieve the Goal

Required Resources

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