AI Face Recognition
Computer vision is the fastest-growing domain of AI and face recognition. These days almost every smart device is using it, including drones and security cameras. However, we need to understand that this isn’t an ‘intelligent’ system. Present-day robots can recognize objects, but can not understand them. For example, face recognition using AI can help a camera to identify a human face. However, it can’t differentiate between a real face and a picture.
Furthermore, before diving into the topic, it is important to understand the current scenario. The evolution of photography has a major influence in today’s smart cameras. Technologies such as ai face recognition and augmented reality rely heavily on cameras. That is why we need to design special cameras for robots, which will be the next step in the evolution of computer vision.
How do Robots See? : AI Face Recognition
Unlike the human eye, smart cameras perceive things in a peculiar manner. Instead of colors and shapes, they process images in the form of matrices. In layman’s terms, matrices are lists of numbers (in this case 1’s and 0’s). These matrices depict color variations in the image. Furthermore, each number corresponds to a pixel as well as a color.
For black and white images, the matrix is 1 dimensional, whereas, for colored images, it is a 3-D matrix. Each dimension indicates the red, green, and blue colors. Now these matrices serve as an input for AI and face recognition algorithms. Convolution neural networks and ML algorithms are one of the most prevalent computer vision algorithms. Moreover, just like infants, computers and smart cameras learn through experience.
What is the Problem and its Solution?
Besides lack of ‘understanding’, hardware compatibility is a major problem for smart devices. Object and face recognition using AI demand real-time computation. Even a one-second lag can lead to massive disasters. Self-driving cars and security cameras can’t afford such loopholes. Therefore, we have to come up with a more innovative solution.
Researchers like Julie Chang are working on new methods of smart vision. She argues that instead of image matrices, we can make cameras themselves as part of algorithms. This concept is known as the ‘Optical Electronic Neural Network’.
Surprisingly, it is a hardware that resembles a camera lens, and it has physical filters which do the same work as that of neural network layers. This makes it a hybrid of computer chip and a camera lens. Concepts like the interference of light and image resolution streamline things like face recognition using AI. These filters decompose a single image into multiple forms resembling different objects at a fundamental level.
Despite the progress in AI and face recognition, we still need to work on this domain. Focusing on software as well as hardware is the most feasible solution. The concept of ‘Optical Electronic Neural Network’ is a gigantic step towards designing robotic cameras. In the coming years, it'll take over the field of computer vision. Furthermore, every researcher and tech enthusiast should definitely keep an eye on these developments.