Special Issue(24-12):Comparative Study of Machine Learning Algorithms for Facial Recognition Systems | International Journal of Automation and Smart Technology

Comparative Study of Machine Learning Algorithms for Facial Recognition Systems

Convolutional Neural Networks are thought to be the finest algorithm for facial recognition because of their superior capacity to extract features and recognize faces in photos. The individual is predicted using a different CNN by using the computed angles and ratios. Facial biometrics are employed to accurately identify the individual. The face is recognized using the kilometre nearest neighbour algorithm in order to do this. The core of contemporary facial recognition systems is artificial intelligence, namely machine learning and deep learning algorithms. The system can learn from enormous volumes of data and get better over time because of these algorithms. It functions by recognizing and quantifying face features in a picture. A machine learning model accuracy is dependent on a number of variables, such as the type and volume of data, the algorithm selected, and the proper adjustment of the model hyperparameters. There is not a single optimal algorithm that consistently performs more accurately than the rest. The programming language Python became the most widely used programming language for facial recognition, probably as a result of its user-friendly interface and flexibility. It is a great option for designing and prototyping facial recognition applications due to its popularity among developers. No additional training is needed because the OpenCV built in Haar Cascade classifier has already been trained on a sizable dataset of human faces. The capacity of CNNs to create an internal representation of a two-dimensional image is a benefit. As a result, the model is able to determine the scale and location of faces in an image. Following training, CNN can identify a face in an image.

Convolutional neural networks are useful for processing image data. Convolutional neural networks are among the most popular and effective machine learning techniques for image recognition. Multiple layers of artificial neurons make up CNNs, which are designed to extract characteristics from images, including objects, edges, forms, and colours. CNNs are the most widely used and efficient classifier technique in machine learning because of these complementing phases. Within the domain of biometric security is facial recognition. Voice, fingerprint, and iris or retinal recognition software are examples of additional biometric software. Though interest in other applications is growing, security and law enforcement are the primary uses of the technology. A machine may learn, anticipate, identify patterns, or classify data by being shown a vast amount of data. This process is referred to as machine learning. Supervised, unsupervised, and reinforcement learning are the three categories of machine learning.

For deep learning, multilayer perceptron’s are the optimal algorithm. It is among the earliest deep learning methods that Instagram and Meta, among other social media platforms, use. In order to identify human faces, facial recognition technology and biometrics, usually using AI, are used. It maps the features of the face from an image or video, then looks for a match by comparing the data with a database of recognized faces. Face recognition software uses machine learning techniques to recognize facial traits in an image or video in order to detect faces. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Comparative Study of Machine Learning Algorithms for Facial Recognition Systems.

Suggested research and application topics of interest include but not limited to:

A comparative analysis of facial recognition machine learning methods.
A comparison of facial recognition algorithms using deep learning and machine learning.
A comparison of face recognition algorithms in terms of illumination and expression.
A comparison of face detection and recognition methods based on machine learning.
Comparative comparison of face recognition using various machine learning techniques.
An analysis comparing facial biometric recognition using deep learning and conventional machine learning methods.
An analysis comparing facial emotion recognition techniques using deep learning and machine learning.
A comparison of facial expression recognition algorithms and techniques.
An Analysis of Machine Learning Classifiers' Performance in Combining Feature Extraction with Face Recognition.
A comparison of machine learning techniques for crime detection and privacy protection using picture recognition.
A comparison of several cutting-edge face recognition methods with different expressions on the face.
A comparison of machine learning techniques for human identification based on lip prints.