▲Eye tracking
Eye tracking is a scientific application technology that allows users to turn pages without touching the screen. In principle, eye tracking primarily studies the acquisition, modeling, and simulation of eye movement information, and its applications are quite broad. Devices that can acquire eye movement information include not only infrared devices but also image acquisition devices, and even the cameras on ordinary computers or mobile phones, which can achieve eye tracking with software support.
▲ Fingerprint recognition technology
Fingerprint recognition refers to identifying individuals by comparing the detailed feature points of different fingerprints. Fingerprint recognition technology involves numerous disciplines such as image processing, pattern recognition, computer vision, mathematical morphology, and wavelet analysis. Because everyone's fingerprints are unique, and even among the ten fingers of the same person, there are significant differences, fingerprints can be used for identity verification.
▲Face recognition
It is a biometric technology that identifies individuals based on their facial features. It uses cameras or webcams to capture images or video streams containing human faces, automatically detects and tracks faces within the images, and then performs a series of related facial recognition techniques. This is also commonly referred to as facial recognition or face detection.
A facial recognition system mainly consists of four components: facial image acquisition and detection, facial image preprocessing, facial image feature extraction, and matching and recognition.
▲Retinal and Iris Recognition
The retina is also a biometric feature that requires laser illumination of the back of the eye to obtain the uniqueness of the retinal feature.
The iris is the ring-shaped area located between the black pupil and the white sclera. Once formed during fetal development, it remains unchanged throughout life. Iris recognition technology is used in security equipment (such as access control systems) and in locations with high confidentiality requirements.
▲3D-TOUCH
It can recognize and react to the pressure applied by the user when pressing the screen and touchpad. While 3D-TOUCH technology seems magical, it's actually derived from Multi-Touch technology; 3D-TOUCH is simply an upgraded version of Multi-Touch. Besides requiring an upgraded version of multi-touch technology, 3D-TOUCH also needs to work with touch sensors and accelerometers to quickly, accurately, and continuously detect fingertip pressure.
▲Gait recognition
Gait recognition is an emerging biometric identification technology designed to identify individuals based on their walking posture. Compared to other biometric technologies, gait recognition offers advantages such as non-contact, long-range operation and resistance to spoofing. In the field of intelligent video surveillance, it has a greater advantage than facial recognition.
▲Multi-Touch technology
Multi-Touch technology (also known as multi-touch, multi-point sensing, or multi-sensing) is an input technology that allows computer users to control image applications using multiple fingers. It's a technology that combines human-computer interaction technology with hardware devices, enabling human-computer interaction without traditional input devices (such as mice and keyboards). Previously, most Multi-Touch systems could only sense one contact point, limiting their application. Therefore, improvements were made to Multi-Touch technology to sense multiple contact points simultaneously, broadening its application scope.
▲Electroencephalogram (EEG) recognition technology
Brainwave recognition technology uses brainwave signals, a bioelectrical phenomenon that reflects brain activity. Multi-electrode brainwaves collected by devices can be used for personal identification, but the practical application of this technology in personal identification is limited by factors such as expensive equipment and complex operation.
▲ Facial expression recognition
It is the process of using computers to extract and classify facial expression information. This enables computers to infer a person's psychological state based on facial expression information, thereby achieving intelligent interaction between humans and computers.
▲Voiceprint recognition
The vocal organs used when speaking—tongue, teeth, larynx, lungs, and nasal cavity—varie greatly in size and shape from person to person, resulting in differences in voiceprint profiles between any two individuals. This makes voiceprint recognition a method of identity authentication.
Compared to other biometrics, voiceprint recognition has the following advantages:
(1) Voiceprint extraction is convenient and can be completed without the user's awareness, so users are highly accepting of it;
(2) Acquiring voice recognition is inexpensive and easy to use, requiring only a microphone, and no additional recording equipment is needed when using communication devices;
(3) Suitable for remote identity verification, requiring only a microphone or telephone/mobile phone to achieve remote login via network (communication network or Internet);
(4) The algorithm complexity for voiceprint recognition and confirmation is low;
(5) Combining it with other measures, such as content identification through speech recognition, can improve accuracy. These advantages make voiceprint recognition increasingly popular among system developers and users.
▲Gene recognition
With the progress of the Human Genome Project, our understanding of the structure and function of genes has deepened, and this understanding has been applied to personal identification. Because among the 6 billion people in the world, there may be others born at the same time as you, with the same name, strikingly similar appearance, or identical voice; fingerprints may also disappear, but only genes are the unchanging indicators that represent your individual hereditary characteristics.
▲Vein recognition
One approach to vein recognition systems involves acquiring an individual's vein distribution map using a vein recognition device and extracting feature values from the map based on a specialized comparison algorithm. Another approach uses an infrared CCD camera to capture images of veins in the fingers, palms, and backs of the hands, storing these digital images of the veins in a computer system to store feature values. During vein comparison, vein images are captured in real-time, and advanced filtering, image binarization, and thinning techniques are used to extract features from the digital image. A complex matching algorithm is then employed to compare and match these features with the vein feature values stored in the host computer, thereby identifying and confirming the individual's identity.
