In this blog post, we will focus on deepface algorithms and deep learning technology to explore whether machines can recognize faces as well as humans, or even better.
Can machines recognize and distinguish faces as accurately as humans, or even better? To find the answer to this question, we need to look at DeepFace, a face recognition algorithm developed by Meta (formerly Facebook), which has the world’s largest photo database.
DeepFace is a technology that analyzes faces in photos to identify who they are, and it is an advanced algorithm that goes beyond simple image analysis and applies AI-based deep learning technology. Anyone who has used Facebook has probably experienced having their face automatically recognized when uploading a photo and seeing the phrase “Want to tag your friends?” appear. This is thanks to DeepFace’s technology.
Conventional face recognition technology attempted to identify individuals by analyzing the distance and ratio between facial features such as the eyes, nose, and mouth. However, DeepFace is a deep learning-based algorithm that goes beyond simple distance analysis to identify even complex and irregular data. According to performance results announced in 2014, DeepFace’s face recognition accuracy was approximately 97.25%, which was almost equal to the average human recognition accuracy (97.53%). Since then, the technology has become even more advanced, and in some cases, it now exceeds human accuracy.
Deep learning is an AI technology that performs repetitive learning based on large amounts of data and has the ability to distinguish between important and unimportant information. At the root of deep learning is the concept of an artificial neural network. This is a mathematical model of the connections between neurons in the human brain, and DeepFace also adopts this neural network-based algorithm.
Early neural network AI used a “single-layer perceptron” algorithm, which was limited to linear classification of input data and was unable to handle complex problems. In response, DeepFace was constructed based on a “multi-layer perceptron.” This algorithm consists of an input layer, a hidden layer, and an output layer, and the deeper the hidden layer, the more complex and sophisticated patterns it can learn. Thanks to this structure, DeepFace can accurately recognize faces even with various variables such as lighting, facial expressions, and angles.
An example that demonstrates the accuracy of this technology is when DeepFace accurately identified Sylvester Stallone’s face printed on a piece of paper. The algorithm accurately captured even the differences in angle and contrast that are difficult for humans to distinguish.
However, DeepFace technology is not yet perfect. Currently, DeepFace can only determine whether the person in a specific photo is the same person. In other words, it cannot yet explicitly identify the name of the person in the photo. However, technology is constantly evolving, and it is expected that this limitation will soon be overcome, enabling real-time face recognition.
The development of facial recognition technology raises both positive expectations and concerns. Facebook founder Mark Zuckerberg said, “Within the next five years, Facebook’s AI technology will fundamentally change our experience.” In fact, deepface has the potential to be applied to various fields, such as searching for missing persons, criminal investigations, and immigration control.
However, at the same time, serious concerns have been raised about privacy and personal information protection. There is a possibility that the identities of people in photos could be identified without their permission, and that the information could be misused by third parties. Recently, there has been active international discussion on the use of facial recognition technology as a means of surveillance in countries such as China and the United States. The European Union (EU) has regulated the use of real-time facial recognition in public places since 2021, and some countries are moving toward a complete ban.
Considering these social repercussions and technological developments, we should not simply view deepfake technology as a convenient tool. Deepfakes have reached a level that surpasses human perception, but at the same time, we must carefully consider their use within ethical and legal standards.
In the future, AI-based facial recognition technology will continue to advance and become more deeply integrated into our daily lives. Therefore, we need to prepare for the new experiences that this technology will bring, while also taking a balanced view of its side effects and risks. The future of DeepFace is not solely in the hands of technology, but depends on the concerns and choices of society as a whole.
