Wondering About Face Detection ! Here is the Concept ! (6 Min)

Reading Time: 6 mins Only

“ Face Detection “ word explains itself but what it technically means finding the faces in an image and probably extract them to be used by the face recognition algorithm.

Wow looks like fuzzy and heavy words, but it isn't! You might think if Face Recognition has an algorithm then Face Detection would also have!

Yes! We have an algorithm. Human Face has some common features for all. If you ask a kid how a human face looks like then what he will answer! He states human face like this that " we have an Eyes, Nose, Mouth, an Ear, Beard and bunch of hairs on the head that's how our brain trained naturally. I am right or not?

So by considering those feature Researcher have developed an algorithm that extracts the geometry of human face from the model. Model is a file where the algorithm exists and all probability calculations are done here.

 Do I need a photo to perform this magic? Of course!

Basic Working Blocks Of Face Detection

Wondering why Gray scaling to face! Because we never match the colored pixel. Why? Because making the cropped face grayscale makes it easier for a trainer to generate a pattern of the human face. This was the training of our model where a specific face pattern is stored. Which will be used further.

 Eureka! You got a Face from an Image !!! 

If you are a curious genius then you want a name on Your Face? So let's take a move on the heavy word “Face Recognition”. Sorry, It's a Lighter concept word than you think!

All set till now! We have trained our model so let's use that work. Take a selfie and give it to Computer.

Let's come to some word which we used previously:

Model: Model returns probability scores on the likelihood that the image contains human faces and coordinates locations of where those faces appear with a bounding box.

Input: The image data that you passed through the model to make predictions. Includes id and the image data.

Data: The data sent back with the response; usually includes the detected concepts and corresponding probability values.

Thanks to Pioneers of automated face recognition Woody Bledsoe, Helen Chan Wolf, and Charles Bisson. We can get this topic up!

Now, let's discuss the type of face acquisition techniques we have basically in our environment.

Types of Face Acquisition Techniques

1. Traditional
2. 3-Dimensional Recognition
3. Skin Texture Analysis
4. Facial Recognition combining different techniques
5. Thermal camera

1) Traditional (2-D Technique): An algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features & compares the values with templates to eliminate variances.

2) 3-Dimensional Recognition: This technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.

A new method is to introduce a way to capture a 3D picture by using three tracking cameras that point at different angles; one camera will be pointing at the front of the subject, the second one to the side, and the third one at an angle.

All these cameras will work together so it can track a subject’s facFacioMetricse in real-time and be able to face detect and recognize.

3) Skin Texture Analysis: This emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. This technique turns the unique lines, patterns, and spots parent in a person’s skin into a mathematical space. (mostly used in skins disorder analysis)

4) Facial Recognition combining different techniques: Because each technique has advantages and weaknesses, technology firms have combined conventional, 3D recognition, and Skin Textual Analysis to build recognition systems with improved success rates.

5) Thermal cameras: In this technique, the cameras will simply identify the contour of the head and will overlook any subject accouterments such as glasses, hats, or make-up. Thermal cameras, unlike conventional cameras, can record face pictures even in low-light and nighttime circumstances without utilizing a flash or revealing the camera's position.

Application

Mobile Platform

• Social Media (Tagging Person)
• Face ID Verification Solution

Deployment in Security Services

• Policing
• National Security

BONUS - FacioMetrics

Facial recognition systems have been used for emotion recognition. In 2016 Facebook acquired emotion detection startup FacioMetrics.

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