The powerful facial authentication system from Chooch AI provides a variety of solutions across industries, in healthcare, for payments and touchless check in to work, school or in the travel industry. Facial authentication is an opt-in solution, created for safety and security, not for surveillance.
The facial authentication system from Chooch AI is straightforward, built for rapid integration, with cloud-based training or complete deployments to edge devices.
The system features extremely high accuracy and fast response times that adds a strong layer of security to the enterprise. The added benefit of layered liveness detection means the system cannot be hacked by presentation attacks.
The AI training process is simple: whether you want to recognize 1 or 100,000 different faces, you first collect the facial data you need from images or video frames, and then train the Chooch platform on this data. All data is stored as hashes, not as faces, to ensure security and privacy. Our facial authentication model was built using the biometric features of more than 1 million faces, without racial or ethnic bias.
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Chooch AI is a complete platform for computer vision, providing object recognition, action detection, and much more, including facial authentication. The AI platform provides end-to-end services – data collection, AI training, AI model creation and containerization, and finally deployment to the cloud or edge AI devices – all in one product.
Facial authentication is a hot-button issue, with many people concerned about the security and privacy implications of storing sensitive biometric information.
To improve security and lessen the impact of a data breach, Chooch does not directly store the images of individuals’ faces. Instead, Chooch’s facial authentication models compute a mathematical hash during training based on a person’s facial characteristics. This hash is able to uniquely identify someone with a high degree of confidence. When a live person is present, the facial authentication system computes the hash of their face and compares it with stored hashes to see if there is a match.
Sending data to the cloud is another potential source of privacy issues. If you’re concerned about performing facial authentication on remote servers in the cloud, we can help you run facial authentication training on edge devices on-premises for added security.
Chooch’s facial authentication systems also use liveness detection to guard against potential attacks. Liveness detection techniques ensure that a live person is consciously and intentionally attempting to log into the system, rather than various attempts to gain unauthorized access (e.g. using the face of a sleeping person, or a photograph of a person not present).
Facial authentication can be applied in nearly any situation imaginable. Here are two use cases.
Healthcare: Verifying individuals’ identity is essential in high-stakes situations such as medicine and healthcare. According to a 2016 report, 86 percent of nurses and physicians say that they have witnessed or heard of a case of patient misidentification. What’s more, 9 percent of such cases lead to death or harm for the patient. Facial authentication systems can ensure that patients receive the correct care and the correct prescriptions, as well as limit access to restricted areas for medical personnel.
Workplace security: Facial authentication has many potential applications in the workplace, from physical access through doorways and turnstiles to clocking in and clocking out. As telecommunication becomes more mainstream, facial authentication can be used to verify remote workers’ identity before giving them access to sensitive and confidential documents. The use of facial authentication can also reduce “time theft,” i.e. employees being paid for hours that they did not actually work.
MORE TO COME HERE. Financial Services. Ensure identity for digital signature services, account access, transfers and more. Liveness Detection prevents presentation attacks and can greatly reduce fraud.
Facial authentication is the use of facial recognition to authenticate or verify a person’s identity.
Facial recognition is the use of a person’s face as a biometric feature to match that person against a database of stored faces. Facial recognition systems look at the relative positions, shapes, and sizes of different facial features, including a person’s eyes, nose, mouth, cheekbones, and jawline.
A biometric feature is a human characteristic that serves as a unique identifier of an individual person. Biometric features include a person’s fingerprint, iris patterns, face, voice, hand size, and behavior (e.g. keystroke patterns or gait analysis).
Liveness detection is a feature of facial recognition systems that ensures that the person in question is consciously attempting to log into the system, protecting against various attacks (e.g. showing a picture of the person instead).
A hash is the output of a hashing function that converts arbitrary data into a (nearly) unique hash. Hashes are used for applications such as facial recognition: the AI model does not store the face itself, but instead computes a unique hash of each face based on different characteristics of each person.