Artificial intelligence is the future development trend. Face recognition is the most widely used technology of artificial intelligence. In real life, we use Alipay, WeChat security verification, and smartphone face unlocking functions. identify. As one of the three major elements in the development of artificial intelligence, the role of data cannot be underestimated. Among them, data collection and data labeling are important directions for data to play a role. Data labeling, as the cornerstone of artificial intelligence, determines the quality of machine learning and deep learning. The rapid development of artificial intelligence has spawned a large number of data annotation companies.
What is face recognition technology?
Face recognition technology is based on human facial features. First, judge the input face image or video stream to observe whether there is a face. If there is a face, it is necessary to further give the position and size of each face. And the position information of each main facial organ, and according to the obtained information, further extract the identity features contained in each face, and compare them with known faces, so as to identify the identity of each face.
The technical principle of face recognition
Face recognition technology is divided into three parts, namely face detection, face tracking and face comparison.
Face detection is face detection, which refers to judging whether there is a human face in a dynamic scene and a complex background, and separating it.
Face tracking refers to the dynamic target tracking of detected faces.
Face comparison is to confirm the identity of the detected face or to search for the target in the face database. Simple video is to compare the sampled facial images with the stock facial images one by one, and find the best matching object.
Four characteristics of face recognition
One is convenience. Because the face is a biometric feature, there is no need to carry something like an ID card.
The second is non-mandatory. The recognition process does not even require the cooperation of the subject, as long as the face is photographed, it can be recognized, such as the security field.
The third is non-contact. Face recognition means that people do not need to touch the device, which is safer than fingerprints. Especially after the outbreak, face recognition is extremely important.
The fourth is parallel processing. This refers to processing multiple faces in a photo at the same time, unlike fingerprints and irises, which need to be processed one by one, which effectively improves the efficiency.
Based on the above characteristics, face recognition is being widely used in various fields. We can see the application of face recognition everywhere in our life.
Recognition Algorithm of Face Recognition Technology
Generally speaking, a face recognition system includes image capture, face positioning, image preprocessing, and face recognition (identity confirmation or identity search). The input of the system is generally one or a series of face images with undetermined identities, as well as several face images with known identities in the face database or corresponding codes, and its output is a series of similarity scores, indicating that The identity of the face to be recognized.
Advantages and disadvantages of face recognition technology
Face recognition technology is convenient, accurate, and difficult to forge. Compared with other biometric recognition technologies, face recognition technology has the characteristics of high accuracy and false recognition rate. However, the representation of human face images in the real world is highly variable, so face recognition technology needs to be further improved. The places where the face image is variable include: head posture, occlusion, lighting conditions, age, facial expression, etc. These factors will affect the accuracy of face recognition.
Application Scenarios of Face Recognition Technology
In people's daily life, the application of face recognition has become more and more widespread, as long as people's identities need to be identified, such as: access control systems, security systems, ID cards, unmanned supermarkets, electronic passports, self-service Systems (such as ATM), information security systems, etc.
In recent years, with the rapid popularization of video surveillance, many video surveillance applications urgently need a fast identification technology that is long-distance and in a non-cooperative state. Face recognition technology is undoubtedly the best choice. Using fast face detection The technology can search for faces in real time from surveillance video images, and compare them with face databases in real time, so as to realize rapid identification.
In the future, as long as people's identities need to be identified, face recognition technology may be applied.
The main data labeling method of face recognition: key point labeling
Key point annotation: Face key points are the annotations for the facial features and contour positioning in the image. They are mainly used to locate the key positions of the face, such as face outline, eyebrows, eyes, and lips. An important step in the face recognition process. The number of face key points ranges from 25 points to 109 points, and the number is increasing and becoming more and more refined. The requirements for the basic skills of the annotators and the review ability of the annotation team are also getting higher and higher. The algorithmic accuracy of the face model plays a big role.
JLW Technology provides data collection and labeling solutions for face recognition technology
The development of technology is inseparable from the support of data. Face recognition needs to accumulate, collect and mark a large amount of face image related data, which is used to verify the algorithm and continuously improve the recognition accuracy. Jinglianwen collected and marked "230 Chinese people with 46,000 real and fake face image datasets", "2834 id25506 cross-age image datasets", "20,000 face key point annotation image training sets", etc. can be used in A dataset for face recognition algorithms.
JLW provides high-quality data annotation support for the realization of artificial intelligence technology. The Jinglianwen data labeling platform customizes mature labeling, auditing, and quality inspection mechanisms, and supports voice engineering (voice cutting, ASR voice transcription, voice emotion judgment, voiceprint recognition labeling, etc.), computer vision (drawing box labeling, semantic segmentation, 3D point cloud annotation, key point annotation, line annotation, 2D/3D fusion annotation, target tracking, image classification, etc.), natural language processing (OCR transcription, text information extraction, NLU sentence generalization) multi-type data annotation.
The existing database has 200T sound and text finished data sets, including TTS, ASR, NLU, NLP and other pronunciation dictionaries, and 420T image finished data sets, mainly covering human biometric data (fingerprint, face, gait, iris, etc.), etc. , other data sets 90T, including finished data sets such as vehicles, road scenes, contraband X-ray machines, etc.
At present, Jinglianwen Technology's in-depth partners cover industries such as automobiles, mobile phones, industry, new retail, real estate, home furnishing, finance, security, education, and ecosystems, including many Fortune 500 companies, university research institutions, and government agencies , a leading AI company and a large Internet company, covering mainstream AI fields such as computer vision, speech recognition, and natural language processing.
In the future, JLW Technology will continue to give full play to its unique advantages of high quality and scene-based, deeply cultivate the data collection and labeling industry, continue to improve data collection and labeling capabilities, create more high-quality, high-standard AI data services, and release the value of data elements .
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