# Face Recognition

October 18, 2017

Information and Communication Technologies are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. Indeed, since the first examples of “intelligent” buildings featuring computer aided security and fire safety systems, the request for more sophisticated services, provided according to each user’s specific needs has characterized the new tendencies within demotic research. With data and information accumulating in abundance, there is a crucial need for high security. Biometrics has now received more attention. Face biometrics, useful for a person’s authentication is a simple and non-intrusive method that recognizes face in complex multidimensional visual model and develops a computational model for it.

# Face Recognition Significance

Face recognition is a biometric technique used for surveillance purposes such as search for wanted criminals, suspected terrorists, and missing children. The term face recognition refers to identifying, by computational algorithms, an unknown face image. This operation can be done by comparing the unknown face with the faces stored in database. Face recognition has three stages a) face location detection b) feature extraction c) facial image classification. Face recognition (FR) has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. This is due to its numerous important applications in identity authentication, security access control, intelligent human-computer interaction, and automatic indexing of image and video databases. Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose.

### Typical View of Face Recognition

Typical structures of face recognition system consist of three major steps, gaining of face data, extracting face feature and recognition of face. Figure 1 shows typical structure of face recognition system in which subject under consideration given to the system for the recognition purpose this is consider being acquisition of face image. Later on feature is extracted from the image and finally it is given for the recognition purpose. These steps are elaborated as follow.

Figure 1 Face Recognition Systems

Gaining of Face Data

Acquisition and Processing of Face Data is first step in the face recognition system. In this step face images is collected from different sources. The sources may be camera or readily available face image database on the website. The collected face images should have the pose, illumination and expression etc variation in order to check the performance of the face recognition system under these conditions. Processing of face database require sometimes otherwise causes serious affect on the performance of face recognition systems due changes in the illumination condition, background, lighting conditions, camera distance, and thus the size and orientation of the head. Therefore input image is normalized and some image transformation methods apply on the input image.

### Extracting Face Feature

Feature extraction process can be defined as the process of extracting relevant information from a face image. In feature extraction, a mathematical representation of original image called a biometric template or biometric reference is generated, which is stored in the database and will form the basis (vector) of any recognition task. Later these extracted features used in recognition. A grayscale pixel is considered as initial feature.

### Recognition of Face

Once the features are extracted and selected, the next step is to classify the image. Appearance-based face recognition algorithms use a wide variety of classification methods Such as PCA, LDA. In classification the similarity between faces from the same individual and different individuals after all the face images in database are represented with relevant features. Sometimes feature extraction & recognition process done simultaneously.

### Face Recognition Applications

• Security: Access control to buildings, airports/seaports, ATM machines and border checkpoints; computer/ network security; email authentication on multimedia workstations.
• Surveillance: A large number of CCTVs can be monitored to look for known criminals, drug offenders, etc. and authorities can be notified when one is located; for example, this procedure was used at the Super Bowl 2001 game at Tampa, Florida; in another instance, according to a CNN report, two cameras linked to state and national databases of sex offenders, missing children and alleged abductors have been installed recently at Royal Palm Middle School in Phoenix, Arizona.
• General Identity Verification: Electoral registration, banking, electronic commerce, identifying newborns, national IDs, passports, drivers’ licenses, employee IDs.
• Criminal Justice Systems: mug-shot/booking systems, post-event analysis, forensics.
• Image Database Investigations: Searching image databases of licensed drivers benefit recipients, missing children, immigrants and police bookings.
• Smart Card” Applications: In lieu of maintaining a database of facial images, the face-print can be stored in a smart card, bar code or magnetic stripe, authentication of which is performed by matching the live image and the stored template.
• Multi-media Environments with Adaptive Human Computer Interfaces: Part of ubiquitous or context aware systems, behavior monitoring at childcare or old people’s centers, recognizing a customer and assessing his needs.
• Video Indexing: labeling faces in video
• Witness faces reconstruction.

#### References

[1] Sanjeev Kumar and Harpreet Kaur, “Face Recognition Techniques: Classification and Comparisons”, International Journal of Information Technology and Knowledge Management July-December 2012, Volume 5, No. 2, pp. 361-363

[2] V. Vijayakumari, “Face Recognition Techniques: A Survey”, World Journal of Computer Application and Technology 1(2): 41-50, 2013.

[3] Jafri, Rabia, and Hamid R. Arabnia, “A Survey of Face Recognition Techniques.” JIPS 5.2 (2009): PP. 41-68.

[4] J. N. K. Liu, M. Wang, and B. Feng, “iBotGuard: an Internet-based intelligent robot security system using invariant face recognition against intruder,” IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Volume 35, PP. 97-105, 2005.

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