Face detection is a kind of technology used in the biometric user authentication processes to give access to the right person. For this, they are analysing the facial features of every individual. When it comes to classification, it could be stated as non-facial detection and face detection. Detecting faces in a streaming video a somewhat difficult. “Are you tired of searching about real time face detection using Matlab? Then, turn your faces into delight! You’ve exactly navigated the exact article”

Generally, this technology is widely used in various fields. For instance, smart classrooms are taking attendance of the students by using face detection technology. On the other hand, it is challenging to detect faces in a live video. We are going to propose the ultimate solution for this.

Yes, Matlab is one of the best toolkits which are preferred by our researchers here. At the end of this article, you would get all the needed concepts in real time face detection using Matlab with thought-provoking facts. Our technical crew has lighted up this article with how does face detection works step by step.

How Face Detection Works?

• Step 1: Face Detection
  • Pin points and detects human faces in a given video stream or image
• Step 2: Face Capture
  • It is the process of converting analog info to digital info according to the human facial features
• Step 3: Face Match
  • The verification process of confirming whether the matched image belongs to another person or not

In short, it is the process of verifying the identity of individuals by considering their faces as input. Along with this, they are simply capturing & comparing patterns of the given image or video stream. The face detection mechanism is just like working as a human brain region which permits us to save the patterns recognized.

The successful detection depends upon the accurate facial feature presence. However, the system is suffering from several issues utilizing real time challenges. Now we can have the section regarding the same. Shall we get into that section? Come on!

What are the Challenges in Real-time Face Detection?

• Frontal Obstacles in Face
  • Wearing masks, spectacles & placing books in front of the face is causing difficulties in relevant feature detection
• Head Positions
  • Viewers can face head pose variations as it is subject to the 3D spaces
  • So that, they are resulting in partial feature acquisition (half face)
• Image Alignment
  • Acquired images may be mirrored or varied in nature leads to present hindrances

However, these constraints can be tackled by accustoming several techniques to that processes. For example, in the viola jones method objects are detected and classified as in the form of Haar features. Similar to this, we can adapt to the different methodologies in every process. Now, we would like the make the section interesting by discussing real time face detection using the Matlab tool. Come on! Let us get into that passage.

Real Time Face Detection using Matlab

Face detection is one of the important processes in surveillance systems & computer vision technologies. Here, we are going to illustrate to you a live video streaming object detection using the KLT algorithm in the Matlab tool.

For this, we need a webcam’s deployment which will be further supported by Matlab’s hardware packages and that can be downloadable online. We need to consider the 2 of the main modes. Such as,

• For detecting :  vision_cascade_object_detector
• For tracking :  vision_point_tracker

If a face is detected in streaming video, then we need to find out the corners of facial features. It is the major reason behind running the tracking function. Point tracker is the significant component which is helping to detect the corners of human faces. If the tracked face fails to meet the threshold stage, then we need to again process the detection processes. Now, we are going to showcase to you that how exactly objects are detected and then tracked. Set up are the process of object creation, face detection, facial point tracking, acquisition, Video processing & display of images/video frames.

• Object Setup
  • Face Detector Object Creation
  • Point Tracker Object Creation
  • Webcam Object Creation
  • Single Frame Size Capturing
  • Video Player Object Creation
• Face Detection & Tracking
  • Capture the Immediate Frame
  • Mode of Detection
  • Finding Detected Region’s Corner Points
  • Rebooting Point Tracker
  • Save Replicas (Copies) of Points
  • Make Bounding Box to Display
  • Face Orientation or Alignments
  • Conversion of Bounding Box Corners as (X1, Y1) (X2, Y2) (X3, Y3) (X4, Y4)
  • Insert shape Formats
  • Displaying Bounding Box around Detected Faces
  • Showcase Detected Corners
  • Mode Of Tracking
  • Estimation of Symmetrical Changes between Former Points & New Points
  • Application of Transformation to Bounding Box
  • Conversion of Box Corners as Per Insertshape Requirements
  • Displaying Bounding Box around Tracked Faces
  • Showcase Tracked Points
  • Rearrange Corner Points
  • Displaying Interpreted Video Frames by Video Player Objects
  • Confirm Video Player Window Conditions
  • Clear-Out Face Detector, Point Tracker, Video Player & Cameras

Bounding boxes are getting the ability to display utilizing converting rectangle presented as x, y, w, h into the M / 2 matrix such as x & y which is coordinating all the 4point corners. The second process is capturing and preprocessing the video frames of the webcam.

In short, it is the loop that runs and detects faces until the video stream got to the end. This is how the system is being worked on to detect and track objects in a given video stream. In addition to these processes, we can make use of the Matlab toolboxes in real time face detection, and some of them are listed below for your understanding.

Matlab Toolboxes for Real-Time Face Detection

• ‘Image Acquisition’ Toolbox
• ‘Computer Vision’ Toolbox
• ‘Image Processing’ Toolbox

The above listed are the various toolboxes used in real time face detection using matlab simulink. These toolboxes can run properly with the support of web cameras. In this regard, how do the webcams support Matlab tools in detecting faces in live videos streams such as Google meet, YouTube, and other surveillance camera footages?

Webcam Support in Matlab for Live Image and Video Acquisition

• Configuration of webcams with local devices will enable to utilize the webcam list to detect cameras
• Image Acquisition from webcams are allowing the user to capture the fluctuating (live) images from cameras as well as facilitating to input the same into Matlab
• Setting up of webcam image acquisition properties either as camera-specific or webcam specific

This is how the webcam supports Matlab for live video frames or image acquisition in general. Before getting involved in the process, one should consider the above-listed testimonials. On the other hand, you may get arise with some questions like which platforms they are good at supporting? Yes, we are going to clear your ambiguities here. Supported platforms of Matlab webcam are as follows,

• Linux
• Microsoft Windows (64 bit)
• Mac OS X (64 bit)

Matlab is compatible with the above-listed operating systems significantly. As this article is titled with the real time face detection using Matlab, we would like to make the next section with the irreplaceable functionalities of Matlab projects in real time video capture.

Matlab Functions for Real-Time Video Capturing

1. Load, Save & Display Video
   • Vision_Video_File_Reader
     • Reads the audio samples in the given video file
   • Vision_Video_File_Writer
     • Writesaudio samples & video frames to the files
   • Vision_Binary_File_Reader
     • Reads video details given in the binary files
   • Vision_Binary_File_Writer
     • Writes binary video frame data to the files
   • Vision_Deployable_Video_Player
     • Video player displays the video clips
   • Vision_Video_Player
     • Even displays images & videos
2. Tracking Object
   • Assign_Detections_To_Tracks
     • Multi-object tracking by assigning detections
   • Bbox_2_Points
     • Conversion of rectangle shape into corner points list
   • Configure_Kalman_Filter
     • Configuration of Kalman filter for tracking objects
   • Vision_Kalman_Filter
     • Rectification on state valuation error covariance & measurements
   • Vision_Histogram_Based_Tracker
     • Object tracking by Histogram
   • Vision_Point_Tracker
     • Kanade-Lucas-Tomasi based points tracking in video frames
   • Vision_Block_Matcher
     • Estimation of motion among videos and images
   • Vision_Template_Matcher
     • Locates the appropriate template in a given image
3. Estimation of Motion
   • Optical_Flow_LKDoG
     • Lucas-Kanade derivative of Gaussian algorithm based optical flow estimating object
   • Optical_Flow_LK
     • Lucas-Kanadealgorithm based optical flow estimating object
   • Optical_Flow_Farneback
     • Farnebackalgorithm based optical flow estimating object
   • Optical_Flow
     • Optical flow matrices storing object
   • Optical_Flow_HS
     • Horn-schunckalgorithm based optical flow estimating object
   • Vision_Block_Matcher
     • Estimates the motion between videos and images
   • Vision_Template_Matcher
     • Locates the template in a given image or video frame
4. Display & Visualization
   • Insert_Text
     • Inserting text into video or image
   • Insert_Object_Annotation
     • Annotation of video and images utilizing either grayscale or true color
   • Insert_Shape
     • Inserting shapes into neither video nor image
   • Insert_Marker
     • Inserting markers into either video or image

These are the 4 major steps involved in real time face detection using the Matlab tool. To be honest, our researchers in the institute are very much aware of these functions and do know every edge comprised in it.

In other words, they are world-class engineers who can dynamically perform under various circumstances. In addition to these sections, we are here going to let you the various datasets used in face detection processes according to the dimensionalities.

Latest Face Detection Datasets

• 2D Face Detection Datasets
  • CAS PEAL
  • CVL Database
  • FRGC Database
  • Yale-B
  • MIT CBCL
  • SCface
  • FEI
  • Casia
• 3D Face Detection Datasets
  • 3D RMA
  • Texas 3D FRD
  • Bosphorus
  • GAVA Database
  • UMB Database
  • McGill Faces
  • 3D MAD

The above-listed datasets are robustly used in face detection processes according to the acquired inputs dimensions (image or video frame). In face detection, systems use computer vision by making the extensive human faces as inputs.

Datasets are being aided to enhance the face detection and face recognition processes in general. In this case, we are going to illustrate some of the popular datasets being practiced by many world-class engineers to make your brain pockets full.

IMDB WIKI

• Description
  • This dataset is consists of gender-wise age labels with appropriate faces
• Number of Images
  • The total no. of images presented here is 5,23,051
• Major Tasks
  • Age Assumptions
  • Face Recognition
  • Face Detection
  • Gender Classification

SOF

• Description
  • SOF is the group of synthetic filters and has the images which as accessories and illuminated under various conditions
• Number of Images
  • The images were captured from 112 individuals 46 from females & 66 from males
  • The total no.of images presented here is 42,592 (16 synthetic images*2662 original images)
• Major Tasks
  • Age Detection & estimation
  • Face Recognition
  • Face Detection
  • Gender Classification

3D RMA

• Description
  • It has the images of 100 subjects who express neutral emotions even with different postures
• Number of Images
  • The total no. of images presented here is 9,971
• Major Tasks
  • Classification
  • Face Recognition

GavabDB

• Description
  • It is the complete 3D images with neutral frontal face expressions with 61 samples per subject
• Number of Images
  • The total no. of images presented here is 549
• Major Tasks
  • Classification
  • Face Recognition

Face Detection Grand Challenge

• Description
  • This is containing basic emotions (7) with 22 samples per subject
• Number of Images
  • The total no.of images presented here is 5,23,051
• Major Tasks
  • Classification
  • Face Recognition

BU-3DFE

• Description
  • This dataset is consists of 7 basic emotions as in the form of 3D with neutral expressions
• Number of Images
  • The total no. of images presented here is 2,500
• Major Tasks
  • Classification
  • Facial Expression Recognition

CASIA NIR

• Description
  • It is the dataset which is only having common emotional images
• Number of Images
  • The total no. of images presented here is 480
• Major Tasks
  • Classification
  • Face Recognition

CASIA 3D Face Database

• Description
  • This dataset is similar to the CASIA NIR as well as consist of closed eyes images
• Number of Images
  • The total no. of images presented here is 4.624
• Major Tasks
  • Classification
  • Face Recognition

UOY 3D-Face

• Description
  • It is the dataset which is having expressions such as sad, happy & anger with raised eyebrows and closed eyes
• Number of Images
  • The total no. of images presented here is 5,250
• Major Tasks
  • Classification
  • Face Recognition

Bosphorus – have to shuffle-it is in the same location

• Description
  • Bosphorus is the database of complete 3D facial images
• Number of Images
  • The total no. of images presented here is 4,652
• Major Tasks
  • Classification
  • Face Recognition

Skin Segmentation

•  Description
  • Skin segmentation dataset arbitrarily labeling the face images according to color values
• Number of Images
  • The total no. of images presented here is 2,45,057
• Major Tasks
  • Classification
  • Segmentation

BioID Face Database

• Description
  • It has facial images with marked eye positions of different individuals
• Number of Images
  • The total no. of images presented here is 1,521
• Major Tasks
  • Face Recognition

FaceScrub

• Description
  • This dataset is the set of public individuals images that are acquired through image searching
• Number of Images
  • The total no. of images presented here is 1,07,818
• Major Tasks
  • Face Recognition

JAFFE Facial Expression Database

• Description
  • This dataset has the images of 10 models who based from japan
• Number of Images
  • The total no. of images presented here is 213 with 6 basic and the rest as a neutral expression
• Major Tasks
  • Facial Expression Cognition

Cohn-Kanade AU-Coded Expression Database

• Description
  • This dataset is the package of labeled expressions of various face images
• Number of Images
  • The total no. of images presented here is more than 500
• Major Tasks
  • Facial Expression Analysis

Yale Face Database

• Description
  • It has the acquired images from 15 persons with 11 diverse expressions
• Number of Images
  • The total no. of images presented here is 165
• Major Tasks
  • Face Recognition

SCface

• Description
  • It is the different angled images with various color patterns
• Number of Images
  • The total no. of images presented here is 4,160
• Major Tasks
  • Face Recognition
  • Classification

FERET

• Description
  • Acquired images are subject to different time variations
• Number of Images
  • The total no. of images presented here is 11,338 from 1,199 persons
• Major Tasks
  • Face Recognition
  • Classification

The foregoing section has clearly stated to you about different types of datasets in face detection. Inputs taken here is mostly video frame and images. We need to give weightage to the performance of the system. That means we need to evaluate the performance of the face detection model. Some of the measures have been listed in the subsequent section for ease of your understanding.