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How does facial recognition technology determine facial attributes?
In recent years, smartphones equipped with facial recognition technology have become a major trend in product development across leading manufacturers. Apple has long been at the forefront of setting smartphone standards and technical benchmarks. Since 2009, the company has actively invested in research and development of facial recognition. By 2017, Apple had acquired seven companies specializing in facial recognition technology, showcasing its strong commitment to advancing this field.
Facial recognition involves several key steps: first, capturing and inputting an image using a camera; then detecting faces within the image to determine if a face is present. Once confirmed, facial features are extracted and compared against known faces in a database to identify or verify a person’s identity. Efficient image processing during the detection phase helps reduce computational load for later recognition tasks and improves overall accuracy. The quality of feature extraction plays a critical role in the success of the recognition process.
From an application perspective, facial recognition used for identifying or confirming someone's identity is referred to as "identity authentication." Additionally, the technology can analyze various facial attributes such as age, gender, expression, and even emotional states—this is known as "attribute analysis."
The technology requires processing large volumes of images, which demands significant computational power. However, advancements in computing performance, including cloud computing, have made it more feasible to handle these tasks. Cloud-based systems enable efficient security monitoring and other applications that require processing vast amounts of image data. On mobile devices like smartphones, facial recognition is increasingly practical for tasks such as photo organization and user authentication.
Apple has applied facial recognition in several ways since 2009. In 2009, iPhoto introduced a feature that could recognize and group faces in photos. The iPhone 4S, released in 2011, featured a face detection camera that could detect up to 10 faces at once. iOS 7, launched in 2013, supported head-motion control, allowing users to navigate their devices by shaking their heads. In 2016, iOS 10 introduced local photo sorting on smart devices. In 2017, Apple launched the Clips app, which used facial recognition to help users organize photos based on who appeared in them.
Starting in 2009, Apple focused more on developing facial recognition technology, initially acquiring patents from other companies. Its own R&D efforts were mainly concentrated on face detection and preprocessing. To speed up commercialization, Apple adopted an M&A strategy in 2010, acquiring technologies that filled gaps in its facial recognition capabilities. By 2017, Apple had built a comprehensive facial recognition system.
Key acquisitions included Polar Rose and RealFace, which enhanced Apple's identification accuracy and speed. Polar Rose's 3D facial recognition, PrimeSense's 3D imaging, and LinX's high-precision sensors enabled Apple to develop 3D facial recognition. Perceptio and Turi provided AI foundations that helped improve accuracy, with Perceptio enabling AI systems to run on mobile devices. These technologies are expected to further enhance RealFace’s AI-driven facial recognition.
Apple aims to develop a 3D facial recognition system powered by AI, suitable for integration into smartphones. Future products may use this technology to enhance multimedia functions, streamline operations, and make devices more intelligent. Applications such as device control and content access via facial recognition are expected soon. Additionally, using expressions and sentiment analysis to create more emotionally engaging interactions between users and devices will be a key focus in the coming years.