Advanced Face Identification Using ML and AI [2022]

One of the most popular uses of computer vision is face identification. It is a fundamental issue with pattern recognition and computer vision. Several face feature Identification techniques have been developed over the past ten years. Recently, Convolutional neural networks (CNN) and deep learning have demonstrated excellent achievements in powering extremely accurate face identification algorithms.

I’ll give an overview of the following subjects:

• Face Identification introduction
• The best Face Identification techniques today
• The pitfalls of real-world face Identification
• Applications of face Identification (Use Cases)

Teksun Telep Face Identification AI model is an AI-enabled smart solution. A solution helps to identify and verify faces to recognize the person’s identity. It provides real-time face Identification, tracking, and monitoring capabilities. Additionally, it uses the existing database of faces in real-time and compares them with the face detected to find a match through advance technologies.

What is Face Identification?

All facial analysis techniques, including face alignment, face recognition, face verification, and face parsing, start with AI Face Identification. Additionally, facial recognition is employed in various applications, including intelligent human-computer interfaces, video conferencing, crowd surveillance, video coding, and content-based picture retrieval.

The digital image ignores other objects like bodies, trees, and buildings. Face identification is an example of object-class detection, the process of identifying the locations and dimensions of all objects in an image that fit into a particular category.

Human face identification is a challenging computer vision task. The human face is a dynamic object with a lot of visual fluctuation. Facial recognition technology has advanced significantly in recent years. High-performance face identification is still difficult, especially with so many small faces.

The two methods for identifying face features are feature-based and image-based. 

Feature-based approach Technique: For identification, feature-based approaches look for invariant traits in faces. The fundamental premise is based on the observation that can be easily detected by a human vision in various positions and lighting circumstances, indicating that there must be constant characteristics or features despite these variations. Numerous techniques have been developed to identify facial traits and deduce the presence of a face. Examples: Facial features like the eyes, nose, mouth, brows, skin tone and hairline are frequently extracted by edge detectors. Based on the collected features, statistical models were created to explain their correlations and confirm the presence of faces in images.

Advantages: The standard strategy is simple to adopt. 

Disadvantages: The fact that picture features can be substantially distorted by lighting, noise, and occlusion is a major issue with feature-based algorithms. Additionally, faces can have weak feature boundaries and sharp edges from shadows, which makes perceptual grouping algorithms worthless.

Image-based approach Technique: 

Image-based learning strategies aim to extract templates from visual examples. Therefore, appearance-based methods rely on statistical analysis and machine learning methods to identify the pertinent traits of “face” and “no-face” photos. Learning characteristics are applied in the form of distribution models or discriminant functions for tasks involving face identification. Examples: Neural networks (CNN), support vector machines (SVM), and Adaboost are a few examples of image-based methods.

Advantages: Good performance with higher efficiency.

Disadvantages: Usually, dimension reduction is necessary for computation and identification effectiveness. This entails acquiring a collection of primary features while preserving the valuable characteristics of the initial data and lowering the dimension of the feature space under consideration.

Why is Face Identification difficult? 

Challenges in face Identification are the reasons which reduce the accuracy and Identification rate of facial recognition. These challenges include a complex background, too many faces in images, odd expressions, illuminations, less resolution, face occlusion, skin color, distance, orientation, etc.

Strange expression: There may be surprising or peculiar facial expressions on human faces in a photograph. 

Illuminations: Some image elements could have very high or low illumination or shadows. 

Skin types: It isn’t easy to detect faces with varying facial hues. Therefore more diverse training photos are needed. 

Distance: The object size (facial size) may be too tiny if the distance to the camera is too great. 

Orientation: The face identification rate is influenced by the face’s position and camera angle.

Complicated history: Many objects in a scene decrease the accuracy and rate of identification. 

An image with numerous faces: Accurate identification of human faces in many human faces is quite difficult. 

Occlusive face: Glasses, scarves, hands, hair, caps, and other things can partially obscure faces, which lowers the identification rate. 

Low resolution: Image noise or low-resolution images have a detrimental effect on the identification rate.

Use Cases of Face Identification Applications

Security Surveillance

• Theft Alert Generation
• Intrusion Identification
• Fire Identification
• Fire & Smoke Identification
• Human Activity Identification

Concluding Note

Numerous chances and limitless potentialities exist thanks to artificial intelligence and machine learning to contribute to the welfare of the planet. By assisting staff members in taking preventative measures against known offenders, modern facial recognition systems make workplaces safer for both employees and customers.