Face Recognition Thesis

Face recognition is one of the thriving technologies which effectively analysis images and videos to recognize faces. It is presented in every technology to identify the users in order to ensure the correct access to the intended accounts. Smart mobile phones are the greatest example of face recognition. The former decade’s researches have significantly contributed their part to developing face-recognizing techniques. “In this article, we have clearly stated about the things to be considered before writing a face recognition thesis

In the earlier stages, techniques such as pattern recognition & computer vision are tried their best to offer consistent face recognition results by detecting and tracking the faces. In fact, now systems are developed well to perform all the required processes regarding face recognition. As the digital world is subject to security and privacy matters, it is demanding identity recognitions hence face-recognizing systems contributing their superlative features.

At the end of this article, you can able to write your own face recognition thesis. In point of fact, feature extraction is one of the important procedures of face recognition. In other words, it is the process of extracting the necessary data from a given image or video. These data can be used for the identification or verification processes of the subjects with reduced error rates.

Further, Evaluating time/duration & memory consumptions are determining the efficiency of the face recognition processes. In addition, outcomes of the face recognition are dealt with the optimization for the further classification phases. Now we can have the section about how the face recognition procedure works in real-time with clear handy notes. 

Research Guidance to implement face recognition thesis


How Face Recognition Works?

The face recognition system takes input in the form of image or video frames and verifies or identifies the subjects presented in the inputs. For your better understanding, we have vibrantly itemized the steps that are commonly used to recognize the faces of human beings.

The aforesaid are the major steps involved in recognizing the human faces besides there are several approaches that are often used to achieve the predetermined aspects. In that, some of the significant approaches practiced in every step are mentioned for making your understanding as strong.

  • Face Recognition Sources
    • Images
    • Videos
  • Face Detection
    • HCI
    • Image Compression
    • Posture Analysis
    • Face Tracing
  • Facial Feature Extraction
    • Gaze Evaluation
    • Sentiment / Emotion Recognition
  • Face Recognition
    • Feature Geometries
    • Holistic Template
    • Hybrid Approaches

The final stage of face recognition can be done with the help of above listed other steps. In the point of fact, verification (authentication) & identification processes are pillared by these steps. There is a database that is stored with a massive amount of images regarding human faces.

If an image or video is given into the face recognition processes, the system will perform the one-to-many matching and will get the identity report from the database. On the other hand, the identification process is done even if the input is unknown (unidentified), the system would match the individuals with the known individuals (identified) presented in that database. 

In addition, the verification process is performed according to the one-to-one face matching tasks. Here the unknown users are authenticated by the identity claims. This is how face recognition works in general. Moreover, it is also vital to consider the accuracy of the face-recognizing systems. Reach us for more interesting face recognition thesis topics. Here, one of the major factors is illustrated that is affecting the face recognition accuracy levels for your better understanding.  

Factors Affecting Face Recognition Accuracy 

The accuracy of face recognition highly depends upon the selection of the FRT (Face Recognition Technology) techniques/algorithms. Those algorithms are explained below.

  • Feature-based Face Recognition Algorithms
    • Focuses on the facial features like eyes, nose & mouth, etc.
  • Appearance-based Face Recognition Algorithms
    • Appearance algorithms never consider the facial feature reflection & postures
  • Hybrid Face Recognition Algorithms 
    • Hybrid is the combination of feature & appearance Face Recognition algorithms
    • It is intended and proposed to diminish the false rates in recognition

In short, the selection of the Face Recognition algorithm is very important to achieve the great accuracy levels of face recognition. If you are trying your researches in face recognition areas consider handpicking the Face Recognition techniques. If it is failed there might be a lot of chances to face issues. In this regard, let’s discuss the key issues that are presented in the face recognition systems in general for your better understanding.

Key Issues in Face Recognition

  • Illumination / Lighting
  • Effects
  • Facial Expression / Manifestation
  • Facial Feature Occlusion / Obstruction
  • Artifacts in Images
  • Posture Variations / Dissimilarities
  • Image Rotation / Spinning

These listed are some of the main issues that are aroused in facial recognition. In point of fact, facial recognition is a little complex in nature because in recent days high-resolution cameras are having the capacity to renovate the facial feature with the help of their inbuilt features. In addition to that quality of the face, recognition falls under a question mark. In the subsequent passage, we have mentioned to you how we can enhance the facial recognition processes.

How Can Facial Recognition be improved?

Facial recognition processes can be improved according to the quality of images/videos acquired. The quality of the images is manipulated by the factors such as illumination (lighting), occlusion (obstruction) & postures/gestures of the individuals. In fact, these can be achieved with the application of several approaches as mentioned in the upcoming section.

  • Facial Structure Recognition
    • Representation of Components
    • Clustered Structure & Component Representation
    • Global Level Representation
  • Supporting Features
    • Local Supporting Features
    • Global Supporting Features
  • Facial Feature Extraction
    • Manual/Handcrafted Extraction
    • Frequency
    • Image Texture
    • Face Shapes
    • Learning oriented Extraction
    • Bayesian
    • Regression
    • Decision Trees
    • Dictionaries
    • Deep Neural Network
    • Model oriented Extraction
    • Graph & Shape
    • Appearance oriented Extraction
    • Multi-Linear
    • Liner & Non-Linear

Afore listed are the various approaches that are supporting attain the improvements in facial recognition. Our researchers in the concern are well expert in the approaches of face recognition technology. This is becoming possible by conducting so many concurrent pieces of research in every single area of technology.

So the capacities of our engineers are getting multiplicities in various aspects and perceptions. In addition to this area, let us also discuss the classical face recognition algorithms and methods with their pros and cons for the ease of your understanding. 

Classified Face Recognition Algorithms & Methods

  • Video-based Face Recognition
    • Pros of Video-based Face Recognition
      • Redundancies Application
      • Image Enrichment
    • Cons of Video-based Face Recognition
      • Ineffective Analysis
      • Lack of Determining Similarities
  • 3D based Face Recognition
    • Pros of 3D based Face Recognition
      • High Accuracy Levels
      • Resilient Outcomes
      • Illumination-free & Posture-free
    • Cons of 3D based Face Recognition
      • Lack of 2D Image Compatibility
      • Costly for Real-time Applications
  • Face Descriptor based Recognition
    • Pros of Face Descriptor based Recognition
      • Discriminant Feature Learning
      • Minimizes & Maximizes Differences (images)
      • Gels with Low Lighting & Expression Variations
      • Simplified Descriptor Extraction
    • Cons of Face Descriptor based Recognition
      • Lack of Intensity
  • Gabor Wavelets based Face Recognition
    • Pros of Gabor Wavelets Face Recognition
      • Effective Frequency Features
      • Diverse Biometric Application
    • Cons of Gabor Wavelets Face Recognition
      • Impractical High Dimensions
      • Sensitive in Illumination Changes
  • Artificial Neural Networks
    • Pros of Artificial Neural Network
      • Simplified Linear Processes
      • Speed Computations
      • Compatible with Occlusion & Incomplete Data
      • Integration of Negative-free Matrix & Radial Basis
    • Cons of Artificial Neural Network 
      • Huge Training Models
      • Inaccuracy Results
  • Classified based Face Recognition
    • Pros of Classical based Face Recognition
      • Adaptive with Diverse Structures
      • Linear Subspace Projections
      • Great Mahalanobis Distances
    • Cons of Classical based Face Recognition
      • Delicate in Lighting Variations
      • Ambiguity in Neighborhood Selection

The itemized above are the major classical methods used in face recognition. In fact, face recognition techniques are full-fledged in identifying the features and preprocessing the same. Further face regions are extracted and backgrounds of the images are segmented.

A separated image is refined under the processes of contrast detection and feature extraction. Facial point detection (nose, mouth, eyes) helps to accurately identify the individuals. By the way, at this time it will be really helpful to know about the recent face recognition project ideas 

Recent Face Recognition Thesis Ideas

  • 3D & Multi-Dimension based Emotion Recognition
  • Dynamic Facial Posture Detection
  • Face Depression/Sadness Recognition
  • Multi-Level Face Recognition & Clustering
  • Anticipated Spoofing & 3D Face Mask Authentication
  • 3D Face Arrangement & Displaying
  • Multi-Level Face Tracing & Grouping
  • Micro & Dynamic Facial Expression Recognition

The above listed are some of the interesting face recognition thesis ideas, you can also select one of the mentioned ideas to develop your own thesis by considering these ideas as a reference. Here, our researchers of the institute have planned to exhibit the evaluation of the face recognition performance. Come on guys let us try to understand them.

How to Estimate the Performance of Face Recognition?

  • ‘Yes’ and ‘no’ are the two binary decisions in facial verification processes
  • ‘Yes’ signifies similar individuals & ‘no’ signifies the different individuals
  • Errors categorization are done in the verification processes
  • These two binary decisions always give the output in the 4 aspects as,
  • False-negative: Identification of a single individual in 2 unlike persons
  • False-positive: Shows the 2 dissimilar persons by way of the same person
  • True negative: Exactly identifies the 2 dissimilar persons in given images
  • True positive: Identification of identical person is given 2 images

These are some of the important aspects that are needed to be considered while evaluating the performance of face recognition. Thesis writing is one of the major writing works that is required in the master’s degree and PhD academic levels. In fact, thesis writing is nearly similar to the research papers that are proposed in the research areas.

As this article is focused on giving content related to the face recognition thesis we are actually going to state the ways to write a good quality thesis for the ease of your understanding in the subsequent section.

What is the Way to Write a Good Quality Thesis?

  • Gather the previous researches and give your “peculiar examination/analysis” on the “findings”
  • Exhibit your “analytical & critical thinking abilities” and show the “interesting fields” in your thesis
  • Refer to the “earlier researches” to completely discover your own “newfangled propositions”

On the other hand, it is very important to deliver the findings in a structural manner. As the thesis is the experimental study of the proposed technical areas it is to be presented in an organized way. Actually, we have listed the things that are contained in every thesis for the ease of your understanding in the immediate phase.

  • Abstract
  • Introduction or background of the research
  • Literature reviews & related works
  • Research problem findings
  • Methodologies & questions
  • Discussions on outcomes
  • References & final conclusions

The above listed are the ways of constructing a good thesis as well as the contents involved in the structure of every thesis. IN fact, we do encourage the students to act like this as well as help them to attain the levels predetermined. Actually, students from all over the world are being benefited by our services rendered. For your valuable consideration, a pinch of our working style in the areas of dissertation writing has been enumerated in the next section. 

How Do We Write a Thesis?

  • Structuring thesis formats in the referencing panaches like Chicago, Turabian, APA, & MLA
  • Framing the contents with the newfangled ideas & with plagiarism-free
  • Delivering both virtual & offline dissertation writing assistances with privacy
  • Customizing thesis contents as per requirements with supreme quality

This is how we work on thesis writing in fact, this is just a sample of our working manner. Actually, we do have so many interesting fields and assistances for the students of every institution. So far, we have come up with the concepts that are requisites for framing the effective face recognition thesis.  Moreover, we are also expecting you guys to explore more in these areas of technology. If you are really interested then you can approach our technical team at any time and the high-quality thesis guidance is waiting for you. Let’s hurry to avail our services.

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