What Is Image Processing Face Recognition?
Face recognition is a kind of biometric recognition technology based on the facial feature information of a person. A series of related technologies that use cameras or cameras to collect images or video streams containing human faces, and automatically detect and track human faces in the images, and then perform face recognition on the detected human faces, often also called portrait recognition, face recognition .
- Chinese name
- Face recognition
- Alias
- Face recognition, face recognition
- Tool
- Camcorder or camera
- Traditional technology
- Face recognition in visible light images
- Approach
- Face recognition algorithm
- Use
- Identification
- Face recognition is a kind of biometric recognition technology based on the facial feature information of a person. A series of related technologies that use cameras or cameras to collect images or video streams containing human faces, and automatically detect and track human faces in the images, and then perform face recognition on the detected human faces, often also called portrait recognition, face recognition .
Face recognition development history
- The research on the face recognition system started in the 1960s. With the development of computer technology and optical imaging technology after the 1980s, the real entry into the initial application phase was in the late 90s. Technology-based implementation; The key to the success of a face recognition system is whether it has a cutting-edge core algorithm and the recognition result has a practical recognition rate and recognition speed. , Model theory, expert system, video image processing, and other professional technologies. At the same time, it needs to combine the theory and implementation of intermediate value processing. It is the latest application of biometric recognition. Conversion. [1]
Face recognition technology features
- Face recognition
- Traditional face recognition technology is mainly face recognition based on visible light images, which is also a familiar recognition method. It has more than 30 years of research and development history. However, this method has insurmountable shortcomings, especially when the ambient lighting changes, the recognition effect will drop sharply, which cannot meet the needs of the actual system. Solutions to the lighting problem include three-dimensional image face recognition, and thermal imaging face recognition. But these two technologies are far from mature and the recognition effect is not satisfactory.
- A rapidly developing solution is multi-light source face recognition technology based on active near-infrared images. It can overcome the effects of light changes and has achieved excellent recognition performance. The overall system performance in terms of accuracy, stability and speed exceeds 3D image face recognition. This technology has developed rapidly in the past two or three years, making the face recognition technology gradually practical.
- The face is born with other biological characteristics (fingerprints, iris, etc.) of the human body. Its uniqueness and good characteristics that cannot be easily copied provide the necessary prerequisites for identity verification. Compared with other types of biometrics, face recognition Has the following characteristics:
- Non-mandatory: The user does not need to cooperate with the face acquisition device, and can obtain the face image in an unconscious state. This sampling method is not mandatory;
- Non-contact: The user does not need to directly contact the device to obtain the face image;
Concurrency: Multiple faces can be sorted, judged, and identified in actual application scenarios;
In addition, it also conforms to the visual characteristics: "identify people with appearances", as well as simple operation, intuitive results, and good concealment.
Face recognition technology process
- The face recognition system mainly includes four components: face image acquisition and detection, face image preprocessing, face image feature extraction, and matching and recognition.
Face recognition face image acquisition and detection
- Face image collection: Different face images can be collected through the camera lens, such as still images, dynamic images, different positions, different expressions, etc. can be well collected. When the user is within the shooting range of the capture device, the capture device will automatically search for and capture the user's face image.
- Face detection: In practice, face detection is mainly used for pre-processing for face recognition, that is, to accurately mark the position and size of a face in an image. The facial features contained in the face image are very rich, such as histogram features, color features, template features, structural features, and Haar features. Face detection is to pick out the useful information and use these features to achieve face detection.
- The mainstream face detection method uses the Adaboost learning algorithm based on the above features. The Adaboost algorithm is a method for classification. It combines some weaker classification methods to form a new strong classification method.
- In the face detection process, Adaboost algorithm is used to select some rectangular features (weak classifiers) that can best represent the face. The weak classifier is constructed into a strong classifier according to the weighted voting method, and then the trained strong classifiers Cascaded classifiers that form a cascade structure in series can effectively improve the detection speed of the classifier.
Face recognition face image preprocessing
- Face image preprocessing: The image preprocessing for the face is a process based on the results of face detection, processing the image and finally serving the feature extraction. Because the original image obtained by the system is limited by various conditions and random interference, it can not be used directly. It must be image pre-processed such as gray correction and noise filtering in the early stage of image processing. For the face image, the pre-processing process mainly includes light compensation, gray scale transformation, histogram equalization, normalization, geometric correction, filtering, and sharpening of the face image.
Face recognition , facial image feature extraction
- Face image feature extraction: Features that can be used in face recognition systems are generally divided into visual features, pixel statistical features, face image transformation coefficient features, and face image algebraic features. Face feature extraction is based on certain features of the face. Face feature extraction, also known as face representation, is a process of face feature modeling. Face feature extraction methods can be summarized into two categories: one is a knowledge-based representation method; the other is a representation method based on algebraic features or statistical learning.
- Knowledge-based representation methods are mainly based on the shape description of the face organs and the distance characteristics between them to obtain feature data that is helpful for face classification. The feature components usually include Euclidean distance, curvature, and angle between feature points. . The human face is composed of eyes, nose, mouth, chin and other parts. The geometric description of these parts and the structural relationship between them can be used as important features to identify the face. These features are called geometric features. Knowledge-based face representation mainly includes geometric feature-based methods and template matching methods.
Face recognition face image matching and recognition
- Face image matching and recognition: The feature data of the extracted face image is searched and matched with the feature templates stored in the database. By setting a threshold, when the similarity exceeds this threshold, the matching result is output. Face recognition is to compare the facial features to be recognized with the obtained facial feature templates, and judge the identity information of the faces based on the similarity. This process is divided into two types: one is the process of comparing images one-to-one, and the other is the process of identifying and matching one-to-many images.
Face recognition recognition algorithm
- Face recognition
- Face recognition algorithm classification
- Feature-based recognition algorithms.
- Appearance-based recognition algorithms.
- Template-based recognition algorithms.
- Recognition algorithms using neural network.
- Neural network recognition
- A light preprocessing method based on Gamma gray correction was proposed, and the corresponding light compensation and light balance strategies were carried out based on the light estimation model.
- Optimized deformation statistics correction theory
- Correction theory based on statistical deformation to optimize face pose; strengthen iterative theory
- Enhanced iteration theory is an effective extension of the DLFA face detection algorithm;
- Original real-time feature recognition theory
- The theory focuses on the median value processing of real-time face data, so that the best matching effect can be achieved between the recognition rate and the recognition efficiency.
Face recognition recognition data
- Face recognition needs to accumulate a large amount of collected face image-related data to verify the algorithm and continuously improve the recognition accuracy. These data such as A Neural Network Face Recognition Assignment (neural network face recognition data), orl face database, Facial Recognition Database at MIT's Center for Biological and Computational Learning, Facial Recognition Data from the School of Computer and Electronic Engineering, University of Essex, etc.
Face recognition coordination
- The existing face recognition system can obtain satisfactory results under the conditions of user cooperation and ideal acquisition conditions. However, in the case where the user does not cooperate and the acquisition conditions are not ideal, the recognition rate of the existing system will drop sharply. For example, when the face is compared, there are discrepancies with the face stored in the system, such as shaving a beard, changing the hairstyle, adding more glasses, or changing the expression, which may cause the comparison to fail.
Face recognition advantage is difficult
Face recognition advantages
- The advantage of face recognition lies in its naturalness and characteristics that are not detected by the individual being measured.
- The so-called naturalness means that the identification method is the same as the biological characteristics used by humans (and even other living beings) for individual identification. For example, face recognition. Humans also distinguish and confirm their identities through observation and comparison. In addition, natural recognition also has
- Iris recognition
- Undetected features are also important for a recognition method, which makes the recognition method not objectionable, and because it is not easy to attract people's attention, it is not easy to be deceived. Face recognition has this feature. It uses visible light to obtain face image information. Unlike fingerprint recognition or iris recognition, it requires electronic pressure sensors to collect fingerprints, or infrared rays to collect iris images. These special acquisition methods are easy. Perceived and therefore more likely to be deceived by disguise.
Face recognition is difficult
- Face recognition is considered to be one of the most difficult research topics in the field of biometric recognition and even artificial intelligence. The difficulty of face recognition is mainly caused by the characteristics of human faces as biometric features.
- Similarity
- Face similarity
- Volatility
- The shape of the face is very unstable. People can generate many expressions through changes in the face. At different viewing angles, the visual image of the face also varies greatly. In addition, face recognition is also affected by lighting conditions (such as day and night, Indoor and outdoor, etc.), many coverings of human faces (such as masks, sunglasses, hair, beards, etc.), age and other factors.
- In face recognition, changes of the first type should be enlarged and used as a criterion for distinguishing individuals, while changes of the second type should be eliminated because they can represent the same individual. The first type of change is usually called an inter-class difference, and the second type of change is called an intra-class difference. For human faces, intra-class changes are often greater than inter-class changes, which makes it extremely difficult to distinguish individuals using inter-class changes in the presence of interference from intra-class changes.
Main uses of face recognition
- Face recognition is mainly used for identity recognition
Face recognition application prospects
- Biometric technology has been widely used in government, military, banking, social welfare, e-commerce, security and defense. For example, a depositor walks into a bank and makes a withdrawal without a bank card or a password. When he withdraws money at a cash machine, a camera scans the user's eyes and then quickly and accurately Has completed user identification and completed the business. This is a real shot from a business unit at United Bank of Texas, Texas. And the sales department is using the "iris recognition system" in modern biometric technology. In addition, after the "9.11" incident in the United States, anti-terrorist activities have become the consensus of governments of all countries, and it is important to strengthen the security of airports. Visage's face recognition technology has been used at two airports in the United States. It can pick out a face among the crowd and determine whether he is a wanted criminal.
- In the current society, cases of burglary, robberies, and injuries frequently occur. In view of this reason, security doors have begun to enter millions of households, bringing peace to families; however, with the development of society, technological progress With the acceleration of the pace of life and the increase in consumption levels, people's expectations for homes are getting higher and higher, and the requirements for convenience are becoming more and more urgent. Security doors based on traditional purely mechanical designs are difficult to quickly except for their durability. Meet these emerging needs: convenience, door opening records, and more. Face recognition technology has been widely recognized, but its application threshold is still high: high technology threshold (long development cycle), high economic threshold (high price).
- Face recognition products have been widely used in financial, judicial, military, public security, border inspection, government, aerospace, power, factory, education, medical and many other enterprises and institutions. With the further maturity of the technology and the improvement of social recognition, face recognition technology will be applied in more fields.
- 1. Enterprise and residential security and management. Such as face recognition access control attendance system, face recognition security door and so on.
- 2. Electronic passport and ID card. An e-passport program in China is stepping up planning and implementation.
- 3. Public security, justice and criminal investigation. Such as the use of face recognition systems and networks to search for fugitives nationwide.
- 4. Self-service.
- 5. Information security. Such as computer login, e-government and e-commerce. In e-commerce transactions are all completed online, and many approval processes in e-government have also been moved online. At present, the authorization of transactions or approvals is implemented by passwords. If the passwords are stolen, security cannot be guaranteed. But using biometrics, the digital identity and real identity of the parties on the Internet can be unified, thereby greatly increasing the reliability of e-commerce and e-government systems.
Face recognition main products
Face recognition digital camera
- Face autofocus and smile shutter technology: First of all, face capture. It makes a judgment based on the position of a person's head. First, the head is determined, and then head features such as eyes and mouth are determined. By comparison of the feature database, it is confirmed that it is a human face and completes facial capture. Then focusing on the human face for autofocus can greatly improve the sharpness of the photos taken. Smile shutter technology is based on face recognition, completes the facial capture, and then starts to judge the degree of upward curvature of the mouth and the degree of downward curvature of the eyes to determine whether it is smiling. All the above captures and comparisons are done under the condition of comparing the feature database, so the feature database is the basis, which contains various typical facial and smiley feature data.
Face recognition access control system
- Face protection can be used to identify the person trying to enter through face recognition. Face recognition systems can be used for corporate, residential security and management. Such as face recognition access control attendance system, face recognition security door and so on. [2]
- Face recognition access control
- Face recognition access control is a safe and practical access control product based on advanced face recognition technology, combining mature ID card and fingerprint recognition technology. The product adopts a split design, the collection of face, fingerprint and ID card information, biological information identification, and internal and external separation of access control. It is highly practical, safe and reliable. The system adopts network information encrypted transmission, supports remote control and management, and can be widely used in access control security control in key areas such as banks, military, public inspection law, and intelligent buildings. [3]
Face recognition
- Such as electronic passports and ID cards. This may be a future-scale application. ICAO has determined that from April 1, 2010, its 118 member countries and regions must use machine-readable passports. Face recognition technology is the first recognition mode, and this regulation has become an international standard. The United States has required that countries with which it has a visa-free agreement must use an electronic passport system that combines biometric features such as face fingerprints by October 26, 2006. By the end of 2006, more than 50 countries have implemented such a system. The Transportation Security Administration plans to promote a domestic biometric-based universal travel document across the United States. Many European countries are planning or are implementing similar schemes to identify and manage passengers with biometric credentials. An e-passport program in China is stepping up planning and implementation.
- Crowds can be monitored at public places such as airports, stadiums, and supermarkets, such as installing surveillance systems at airports to prevent terrorists from boarding. If the bank's ATM is stolen, the user's card and password will be stolen by others. Applying face recognition at the same time will avoid this situation. Query the target portrait data to find out if there is basic information about the key population in the database. For example, installing systems at airports or stations to arrest fugitives.
Face recognition web application
- Face recognition process (2 photos)
Face recognition entertainment applications
- With the rise of the mobile Internet, some developers of face recognition technology have applied this technology to the entertainment field, such as applying happy star faces, etc., based on the contours, skin color, texture, texture, color, and lighting of the face. Calculate the similarity between the protagonist and the star in the photo.
Face recognition application example
- On April 13, 2012, the tender for the face recognition system project in the Beijing-Shanghai high-speed railway security inspection area began. The high-tech security inspection system for the identification of human faces-faces will be installed in the security inspection areas of Shanghai Hongqiao Station, Tianjin West Station and Jinan West Station. Identification system to assist the public security department in arresting fugitives, face recognition products and high-tech innovative companies in system solutions. The core technology research and development team is formed by experts in this field, focusing on face recognition technology as the core, covering product design and research and development projects such as attendance, access control and security. Today, face recognition products have been widely used in the fields of finance, justice, military, public security, border inspection, government, aerospace, electric power, factories, education, medical care and many enterprises and institutions.
- On September 5, 2013, the face payment system was unveiled at the China International Finance Exhibition. The face payment system is based on the biometric cloud financial platform independently developed by Tiancheng Shengye. It integrates the military-level face recognition algorithm with independent intellectual property rights with the existing payment system, and connects payment, transfer, and settlement in our lives. And transactions. When paying, people no longer need bank cards, passbooks and passwords, or even mobile phones. They only need to nod and smile at the camera. The payment system will complete identity verification, account reading, and transfer payment within seconds. , Transaction confirmation and other one-stop payment links to create a better payment experience for users.
- Beginning in August 2014, Japan will restart experiments on the face recognition system at immigration (border inspection) offices at some airports. The first experiment conducted in 2012 was suspended due to frequent errors. However, the Ministry of Justice believed that the border inspection speed needed to be increased in order to meet the 2020 Tokyo Olympics, so it decided to restart the experiment. The experiment will be carried out for about 5 weeks starting in August 2014, and will be conducted on Japanese people who fly at Haneda Airport and Narita Airport. The company responsible for the experiment will be finalized soon. The Japanese government has set up automatic border check gates that can pass only by fingerprint identification at various airports. However, due to the need to register fingerprints in advance, passenger utilization is not high. Face recognition requires no prior registration. [4]
- The Hannover IT Fair (CeBIT) opened in Germany on March 15, 2015. Alibaba founder Jack Ma as the only invited entrepreneur representative gave a keynote speech at the opening ceremony. After giving a speech, Ma Yun demonstrated the Smile to Pay facial cleansing technology of Ant Financial for German Chancellor Merkel and Chinese Deputy Prime Minister Ma Kai, and brushed his face on the spot to buy gifts for guests. Jack Ma's chosen gift is Taobao