5 Techniques Sophisticating Image Recognition

Much driven by the latest advances in machine learning and an increase in machine computing power, image recognition has taken the world by storm.

FREMONT, CA: The latest technological advances have led to the development of new image recognition ideas, which help in processing and categorizing entities based on trained algorithms, from managing a driverless car to performing face detection for biometric access. The computer vision industry has risen significantly over the years. It is presently valued at $11.94 trillion and is probable to achieve $17.38 trillion between 2018 and 2023, at a CAGR of 7.80 percent. This is due to the growing demand for independent and semi-autonomous cars, wearables for drones (military and domestic purposes), and smartphones. Also, the increasing implementation of Industry 4.0 and automation in manufacturing sectors further boost the demand for Image Recognition. Considering computer vision's increasing potential, many organizations are investing in image recognition to interpret and evaluate information mainly from visual sources for several uses such as medical image analysis, identification of objects in autonomous cars, face detection for safety purposes, etc. With Artificial Intelligence (AI) and trained algorithms, it utilizes machine vision techniques to acknowledge pictures through a camera system.

AI Applications

Various organizations are creating AI-based potent approaches to face recognition. Deep Learning involves algorithms inspired by the neural networks of the human brain. Deep learning enables the use of brain simulations and facilitates the use of learning algorithms. In pattern recognition (PR), the latest improvement in deep learning models has provided new methods to address the problem. PR is an area of science focusing on sequence detection in each input.

Predictive Modeling Techniques

Techniques of predictive modeling are used to integrate facial information to comprehend how people age. A process called "de-aging" has tested the technique, which includes taking an ancient person's image and running the Deep Learning algorithms backward to generate a younger version of the same individual. With the huge quantities of information sets available for studies and the capacity of deep learning algorithms to process, facial recognition technology is underway.    

Convolutional Neural Networks (CNN)

In contrast to a fully connected neural network, the neurons in one layer do not connect with all the other neurons in the next layer in a CNN. Instead, a CNN utilizes a three-dimensional structure in which each set of neurons analyzes a particular area or image feature. CNNs filters proximity relations (pixels are only analyzed concerning neighboring pixels), making the training method achievable computationally. Every neural group focuses on one portion of the picture in a CNN.

Gabor Wavelet‐Based Solutions

Face recognition scientists have commonly used Gabor wavelets for face representation, and Gabor characteristics are recognized as a better representation for face recognition. Also, differences in illumination and speech are shown to be discriminatory and robust. Propose adaptively weighted sub-Gabor range for face depiction and recognition when only one sample picture per registered topic is accessible. In adopting the classifier fusion approach, the Gabor characteristics obtained from each channel as a fresh sample of the same class are used. Such a strategy helps enhance the efficiency of recognition outcomes.

Face alignment

Face alignment plays a vital role in various visual apps. Recent achievements in face alignment and face recognition have been asserted by Artificial Neural Networks (ANN), and other models have shown a success. Interestingly, DL methods can be used to clarify genetic variants for pathogenic organisms to be identified. Combined annotation-dependent depletion algorithm is commonly used to interpret variations of coding and non-coding.

Due to its several apps in different fields, image recognition has got a lot of attention recently. With the infusion of latest technologies and betterments, image recognition has is sure to be the new normal.