Essay Example on the Facial Imaging Software

Facial imaging refers to the use of facial images as a way of human identification. It involves the facial approximation and photographic superimposition as well as the construction of facial graphics from the composite and sketch memories. The facial imaging software is the channel used to match an individual's photograph with the photographs stored for many individuals in a common pool (Jain, & Li, 2011). Its use has expanded from various departments such as employment affairs and social protection to various online platforms such as Facebook providing various advantages and limitations with its implementation.

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Facial Imaging software has well been known to detect potential identity fraud as being compliant with the law. It is important in the modern era for applications including police inquiry. The technology focuses on the facial characteristics of an individual such as the distance between the nose and eyes, or even a freckle on either of the cheeks. This information is converted into a unique algorithm, different from many people's algorithms (Wechsler, Phillips, Bruce, Soulie, & Huang, 2012). During the normal registration process, and the issuance of cards, a face-to-face registration process is used where someone's picture is taken and used on the identity card and the image-matching database. With the use of facial imaging, biometric data is not used, which comprises of unique information for the identification of a person such as their fingerprints (Klontz, & Jain, 2013). Facial imaging uses a simple jpeg image, which does not give the consent of collecting and processing other data categories.

Facial imaging software is used to create an arithmetic template that can be used to detect potential identity fraud (Pham, & Tollefson, 2010). In online platforms such as Facebook, it can be used to recognize the user of an account and suggest whom to tag to the posted photos. The arithmetic template cannot be stored in a public service utility as it is not shared with another party. However, the facial imaging technique reserves the privacy of the individual by storing the images in the facial image matching software's database that can be held in secure data centers of the company or platform using it.

Most of the applications of facial imaging software are compliant with the law and are covered well by the set legislative frameworks such as the Data Protection Act. The only consideration of biometric data in facial image processing is when personal information is processed through a technical means that allow for the unique identification of the natural individual (Pham, & Tollefson, 2010). For online platforms such as Facebook, the project is just as accurate as the human brain. The most recent versions of the facial imaging software can compare two photos regardless of the lighting or angle and provide feedback of whether they contain the same person in them (Xu, Zhang, Yang, & Yang, 2011). The current system can analyze individually uploaded photos and prompt one of the available tags.

There have been various developments with facial imaging software. For instance, Facebook has introduced the Deep Face, which consists of millions of neurons that are at least nine layers deep, which also creates synapses or connections between them based on millions of photos of faces (Taigman, Yang, Ranzato, & Wolf, 2014). The learning process later lets images pass through the synapses in different ways, producing unique fingerprints, important for more precise identification of a face. However, having the system in place should comprise a well-written policy that states how long a company is willing to retain biometric information (Unar, Seng, & Abbasi, 2014). Without the information, that can be termed as a violation of the lawsuit. However, there are challenges in identifying the difference between natural face scanners that create biometric records and the facial imaging technique. There is a potential of tracking someone's face across the web, and even in real life as one moves around such as in malls during shopping, to produce important behavioral tracking data.

Benefits

• The facial imaging software follows the law and the processing of private information such as biometric data regarding an individual's political statue, their ethnicity, race, or religious beliefs are not covered. Many countries prohibit access to such personal information unless where there must be the need for legal requirements.
• The cost of operating a facial imaging software system is "self-financing" in the case of the detection of fraudulent activity (Leyvand, Meekhof, Wei, Sun, & Guo, 2011). The application of facial imaging software is even more than allocated funds where required, letting it be a cheaper security measure.
• No impacts are resulting from different lightning between any two photos. Additionally, the facial imaging software can match a face regardless of the presentation of odd angles through an automatic 3D transformation of the average forward-looking face (Geng, 2011: Heike, Upson, Stuhaug, & Weinberg, 2010).

Drawbacks

• Facial imaging software requires constant upgrades for better performance and functionality improvement. This may be as a result of the algorithm change as a result of the change in the current market standards and expectations.
• For online platforms such as Facebook, tag suggestions can only be provided for forward-facing photos. There could be a lot of false matches resulting from any other photos apart from the forward-facing ones.
• Very high skills in mathematics and programming are required for facial imaging software. The cost of the skills may be high, leading to high costs of starting up the software.

References

Geng, J. (2011). Structured-light 3D surface imaging: a tutorial. Advances in Optics and Photonics, 3(2), 128-160.

Heike, C. L., Upson, K., Stuhaug, E., & Weinberg, S. M. (2010). 3D digital stereophotogrammetry: a practical guide to facial image acquisition. Head & face medicine, 6(1), 18.

Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. New York: Springer.

Klontz, J. C., & Jain, A. K. (2013). A case study of automated face recognition: The Boston Marathon bombings suspects. Computer, 46(11), 91-94.

Leyvand, T., Meekhof, C., Wei, Y. C., Sun, J., & Guo, B. (2011). Kinect identity: Technology and experience. Computer, 44(4), 94-96.

Pham, A. M., & Tollefson, T. T. (2010). Objective facial photograph analysis using imaging software. Facial Plastic Surgery Clinics, 18(2), 341-349.

Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701-1708).

Unar, J. A., Seng, W. C., & Abbasi, A. (2014). A review of biometric technology along with trends and prospects. Pattern Recognition, 47(8), 2673-2688.

Wechsler, H., Phillips, J. P., Bruce, V., Soulie, F. F., & Huang, T. S. (Eds.). (2012). Face recognition: From theory to applications (Vol. 163). Springer Science & Business Media.

Xu, Y., Zhang, D., Yang, J., & Yang, J. Y. (2011). A two-phase test sample sparse representation method for use with face recognition. IEEE Transactions on Circuits and Systems for Video Technology, 21(9), 1255-1262.