Machine Learning and Biometric Systems
Machine learning is the systematic study of scientific algorithms that provide the system with the ability to simulate human learning activities without being explicitly programmed. Machine learning also studies the biometric topographies to simulate an individual's identification learning activities.
Machine learning has made the functioning of biometrics identification possible and has also made much advancement in biometric pattern recognition. Machine learning approaches are further divided into three types: Unsupervised Learning, Supervised Learning, and Reinforcement Learning. These approaches help in the identification, classification, clustering, dimensionality reduction and recognition tasks which are needed to develop biometric systems.
Now, we will learn about the three machine learning approaches more deeply:
What is unsupervised learning?
Unsupervised learning is a part of machine learning that studies from test data that has not been classified, labeled or characterized. Instead of responding to the feedback, unsupervised machine learning identifies the commonalities in the data, and it responds by the presence or absence of such unities in every new piece of information.
Working of Unsupervised Learning.
Biometrics and Unsupervised Learning
The unsupervised scientific algorithms are designed for biometric applications which are mainly focused on specific data protection by encrypting biometric information, biometric data extraction, feature level fusion, behavioral pattern detection among others. Besides, biometric systems which have been implemented by using unsupervised learning ensures better learning policies and registration, successively allowing better classification and exact proof localization of biometric features.
What is supervised Learning?
Supervised learning is a modern technology of learning a function that infers a role from labeled training data which maps an input to an output based on example input-output pairs. In supervised learning, the data consists of a set of training examples where each example is a pair which includes an input vector and the desired output value or the supervisory signal. Supervised learning algorithm studies the training data and produces an inferred function, which is used for mapping new examples.
Working of Supervised Learning
Biometric and Supervised Learning
Supervised learning has been serving for numerous biometric applications by using a large number of algorithms. In contradistinction to unsupervised learning, which only uses mainly K-means algorithm for biometric applications, supervised education offers a variety of approaches for biometric pattern classification principally. Few algorithms of supervised learning are given below:
Face Recognition- The 'Decision Trees' algorithm of Supervised Learning is applied for the exact face recognition Biometrics. According to the latest survey, this algorithm results shows a maximum accuracy of 100% on the FERET dataset and 99% on the CAS-PEALR1 dataset.
Speech Emotion Classification- For independent speaker verification, the 'Support Vector Machines (SVM)' algorithm is used. The baseline accuracy for speech emotion recognition was around 50% to 90% depending on the selected technique.
Facial Emotion Recognition- The 'Kernel Perceptron' Learning approach is used for Facial emotion biometric recognition. The classifier recognizes the 6 different Emotions with 98.6% efficiency on the JAFFE dataset.
Reinforcement learning is a type of dynamic machine learning programming which systematically learns to perform a new task and trains the algorithms using a system of reward and punishment. This learning technology is concerned with the software agents who take necessary actions in a real-time environment to maximize some notion of accumulative reward.
The reinforcement learning algorithm is built on the same concept that a child uses to learn a new task. Similarly, this algorithm learns by interacting with its environment. The software agent automatically receives rewards by performing correctly and penalties for performing inaccurately. The agent is programmed to determine without the intervention of a human only by maximizing its compensation and minimizing its penalty.
Working of Reinforcement Learning
Reinforcement Learning and Biometrics
Reinforcement learning seems to be more versatile than supervised and unsupervised learning. It is useful for both unsupervised labors and supervised labors. However, reinforcement learning is limited to reasonably low dimensional problems. But Deep Reinforcement Learning (DRL) has proven to be useful to solve this problem. Despite the successes of DRL, many issues need to be addressed before these techniques are applied to a broad range of complicated real-world issues.