Face recognition technology is widely used in biometric user authentication processes. This authentication process can be done via human faces features extraction and the classification processes. Here, we are completely going to discuss how face recognition systems works under various circumstances with crystal clear explanations. “Are you tired of surfing about face recognition using Matlab? Congratulations! You’ve navigated to an exact encyclopedia which is fully comprised of interesting facts about the same”
They are using several software and applications for detecting the similarities between the presented inputs (images or video frames). Computerized technologies are commonly using face recognition processes. As you know, even a smart mobile phone is requiring a user’s biometrics (face & fingerprints) for identification purposes.
This is one of the thriving technologies which are having so many advantages such as privacy and security to the digital platform users. This handout is exclusively presented for enthusiasts who are surfing for a long time about face recognition using Matlab. Let us get into the article with the beginning of the fundamentals of face recognition.
Fundamentals of Face Recognition
Facial classification is the process of classifying human faces according to their age, gender, emotion & other related features by using some applications. It is different from face recognition as it is only intended to compare the variations between 2 different images.
Facial characterization is often manipulated as face recognition technology. However, facial classification is the process that has to be done before recognizing human faces. They are different working mechanisms that are working together to attain the best outcomes.
As this article is fully focused on giving face recognition peripherals, we are going to list out you the various facial features for the ease of your understanding. Shall we get into that section? Come on! You will love that section.
What Are Facial Characteristics?
- Mouth Width
- Head Eccentricity
- Eye Eccentricity
- Eyebrow Slant
- Pupil Size
- Mouth Openness
- Mouth Curvature
- Nose Width
- Nose Length
- Eye Spacing
- Eye Size
The aforementioned are the various facial characteristics considered in the process of human face recognition. Yes, with these features we can map out the dimensionalities incredibly. Face recognition technology is allowing the legitimate user to the right services by analyzing their facial features.
Behind every face recognition outcome, there are so many core processes are taking place at the back ends. In fact, face recognition technology is widely used in every phase of technology in order to attain security levels. Here, some of the possible application areas are
mentioned by our researchers for your better understanding.
Applications of Face Recognition
- Domestic Security Mechanisms
- Digital Transaction in E-Commerce
- Medicare Monitoring Devices
- Access Control System
- Surveillance Cameras
- Biometric User Authentication
- Forensic Detection Organism
Itemized above are different applications areas of face recognition technology apart from this there are so many exist. If you are in need of further explanations, you are welcome to have our newfangled suggestions for face recognition using matlab in the determined areas.
Having face recognition technology is resulting in various merits. However, there are several issues that are prevailing when it comes to open research. Yes, the next section is all about the same. Come let us learn some spectated issues by our technical team in their researches.
Open Research Issues in Face Recognition
- Image Acquisition Issues
- Inadequate Feature Extraction
- Poor Lighting & Clarity Conditions
- Background & Foreground Segmentation
- Image Artifacts or Noises
- Diverse Face Expressions
- Makeups, Blemishes & Spectacles
- Skin Color Dissimilarities
The foregoing passage is simply conveyed to you the different constraints arising while recognizing human faces. However, these issues can be abolished by integrating the Matlab tool in the face recognition processes. Here, you may get a question on how the facial features are extracted. In fact, the next is having your dilemmas answered.
How Facial Features are extracted for Recognition?
- Marker Tracing / Tracking
- Deformation measuring techniques in maker tracking will offer supreme consistency in face recognition
- Besides, they are skin region oriented which may result in ineffective outcomes
- Highlighting processes are performed neither by color salient facial / skin features
- Feature Point Tracing / Tracking
- Intransient facial features only being handpicked for motion estimation
- High contrast areas are preferred for situating feature points in order to diminish tracking losses
This is how the feature point and marker racking are pillaring the feature extraction processes. In fact, Matlab is the only tool that can perform face recognition processes accurately. Because they are having enriched syntaxes and user interfaces which allows the users to edit parameters. In this regard, let us see about the unique features of face recognition using matlab process. Come on! Let us get into the next section.
Unique Feature of Matlab for Face Recognition
“Streaming or live feature training, face detection & recognition”
It is the process that is lively exhibits the face image acquisition, feature detection, training & face recognition. In addition, we can link the web cameras with the host to run streaming face recognition. It has the unique functionalities as mentioned below,
- Automated Labeling Processes
- Fast & Robotic Recognizer Training
- Stimuli for Naming Faces
- Recommending Another Captures
- Scripting under Labeled Subdirectories
- Captures Trained Face Images
- Detects Faces Robotically
- Webcam Analyses Live Preview of Users
Afore bulletined are the unique features of Matlab toolkit for face recognition processes. Besides, we hope that you would have understood the concepts as of now listed. The main intention behind our academics is to make the students understanding superior by their tutorials.
As a matter of fact, we do suggest scholars and students pick face recognition matlab processes. As we are conducting so much of the researches in Matlab, we clearly know the importance of that tool. Now is the time to discuss how to detect the faces using the
Matlab tool to make your understanding better.
How to implement Face Recognition using Matlab?
- Configure high resolution (640*480) acquisition tools
- Frame bounding boxes over the face and showcase Hue Channel data
- Further video frames extraction
- Conversion of RGB into Hue Saturation Value (HSV)
- Tracking Features by Hue channel data
- Connect bounding box with tracked objects
- Inset text or data harmonizes
- Present the interpreted video frames by video players
- Execute resources/users
The main reason behind configuring highly-resolution cameras in face recognition tools is to enhance the performance of the system. According to the engineers’ opinion, every technology is getting enhanced by means of modernization. In that, various tools and techniques are being practiced and executed to attain the best results over the technical era.
On the other hand, Matlab is one of the unique programming languages used for the technical researches of face recognition and they are widely addressed for their unique functionalities. Let’s have further discussions about face recognition using matlab processes. Shall we move on to that section? Come on students let us also learn them.
Matlab Function for Face Recognition
1. Face Matching Function
EuclideDist = ()
It is the function for equating & computing the trained images Euclidian distance from test images proposed or projected.
2. Training Images for Face Recognition
function (Recognized_Result_Face_Image) = Face_Recognize_Process (Input_Image, Image_Location)
This function aligns the set of human face images by reshaping 2D images into single-dimensional column vectors. Further 2D matrix (X) is created by again inputting 1D column vectors as in rows
Here, datapath is the way in which data images are trained and X is the matrix that contains complete 1D vectors of image. If the database is presented with similar sizes (M*X) of P images then ‘X’ matrix will be (M*N) XP 2D & the length of the 1D column vector is determined with M*N.
Listed above are only the 2 functions face recognition using matlab processes. Besides, there are so many functions that can be further added according to your requirements. As this is just an illustration, we have listed only 2. If you do want further details in these areas, you can approach our researchers at any time. In the following section, we have stated to you the different methods used for face recognition.
What are the Methods that can be used in Face Recognition?
- Feature Classification
- Feature Extraction – Motion, Edges & Color
- Converting Patches
- Clustering Methods
- Region Segmentation & Localization
- Key Frame Extraction
- Image Fusion
- Image Processing
- Image Stitching & Restoration
- Temporal & Spatial Segmentation
- Image Noise Removals
- Image Enrichment
- Compression Techniques
- 2D & 3D Image Acquisition
- 2D & 3D Image / Video Database Modeling
These are the various methods being utilized in the process of face recognition. As you know very well every technology is using some of the essential algorithms according to their mechanisms in order to enrich performance. However, they are subject to some misfortunes & fortunes. Yes, we are going to illustrate to you the comparisons of different face recognition algorithms for making your understanding easy.
Comparison of Face Recognition Algorithms
- ConvLSTM – Convolutional Long Short-Term Memory
- It is the combination of convolutional neural network & long short term memory proposed to extract local data
- Merging local data with recurrent neural network is the biggest strength of CNN to access temporal frameworks
- It is capturing the transitions among different facial patterns
- Convolutional neural network ->for feature extraction
- Long short term memory layers -> for capturing image sequence variations
- RNNs – Recurrent Neural Networks
- Image classification is done by using dynamic temporal behaviors
- Analyses input samples even with former inputs for executing exact contexts
- It is also considering previous frame’s images as contexts
- CNNs – Convolutional Neural Networks
- Here, image classification is done by using go-to neural networks which are adapting to the different facial patterns
- CNN is applying kernels to each and every chunk of image inputs hence leading to generate newfangled activation matrix
- This is also termed a feature maps which is transferring input to the next levels
- In short, they are processing sensitive components of the images by means of normalization techniques
- SVMs – Multiclass Support Vector Machines
- It is a kind of supervised deep learning technique used to classify and analyze the given inputs further interpret images
- This is only compatible with the images which is captured in a structured manner instead of candid captures
- They are contributing their poor performance in image classification which are captured under impulsive & wild environs
This section is significantly showcased every stated method’s own features under face recognition processes. In fact, our researchers in the institute are very familiar in the areas of face recognition technology with each and every technique.
Actually, we do prefer students to work on novel ideas instead of former ones. Yes, we are going to let you know some of the innovative topics in face recognition processes for your valuable considerations.
Innovative Topics on Face Recognition
- Reconstructing Suspects’ Faces by Sketching Techniques
- Face Recognition based Automated Attendance Mechanisms
- Access Control Systems using Face Recognition
- Digital User Authentication by Face Recognition
The above listed are some of the innovative topics involved in face recognition technology apart from this, there are numerous study areas that are subject to huge explorations. You can look into the face recognition using matlab supporting technologies in order to inject splendid innovations into it. In this regard, let us have a section with current trends.
Current Trends in Face Recognition
- Biometric User Authentication in IoT Devices
- Person Gazing or Staring Valuations
- Fluctuating Face Recognition
- CNN Based Multidimensional Face Generating
This is just a piece of innovation besides interesting fields with novelty is being kept in our pockets. If you are looking for assistance in these areas, you can make your zones comfortable. In fact, we are predominantly lending our helping hands to many of the students and researchers from all over the world.
On the other hand, we also emphasize the students to make sure the performance of the system. Here, you may get confused!!! A system’s performance can be determined by some of the parameters called performance metrics. Yes, the next section is all about it.
Performance Analysis of Face Recognition
Performance of the system can be evaluated through several factors that are involved in the processes of face recognition such as subjects (persons), locality, and illumination (lighting) conditions of acquired images. In this regard, we would like to introduce some of the performance metrics being used to denote the system’s significance.
- ROC Curve
- Equal Error Rate (EER)
- Execution Time
- Computational Complications
- No.of False Negatives and False Positives
Images with various classes can be further examined by the application of confusion matrixes. In confusion matrix adapts to the size of n * n if in the case of n number of persons presented in databases. In addition, diagonal values lie between non-zero entries whereas non-diagonal values possess 0 for enhancing the classifier’s performance. So far, we have brainstormed in the areas of face recognition using Matlab with clear explanations. Are you really interested to know further? In fact, there are many interesting fields such as face emotion recognition using matlab, and more yet to reveal.
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