%0 Journal Article %1 V_2015 %A Salunkhe, Veena V. %A Deshmukh, Mrs. M.P. %A Pimplekar, Prabodhi A. %A Bhosale, Nikita S. %A Bendre, Amruta G. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Biometrics acquisition android anti-spoofing authentication data detection dimensionality encounter extraction face identification identity image malicious management mobile recognition segm spoofing system three verification %N 3 %P 1389--1394 %R 10.17762/ijritcc2321-8169.1503106 %T FAMC Face Authentication for Mobile Concurrence %U http://dx.doi.org/10.17762/ijritcc2321-8169.1503106 %V 3 %X It has been observed in the last decades that face recognition has acquired a large amount of attention and curiosity. Benefits of this have been seen in quite a few applications. An architecture which has been implemented earlier addresses the face analysis domain. As compared to other biometrics, face recognition is more advantageous but it is particularly subject to spoofing. The whole cost of the system increases since the accuracy of this technique involves the estimation of the three dimensionality of faces. An effective and efficient solution for face spoofing has been proposed in the paper. The growing use of mobile devices has been a growing concern due to their ability to store and exchange sensitive data. Thus this has given encouragement to the interest of people, to exploit their abilities, from one side, and to protect users from malicious data, on the other side. It is important to develop and deliver secure access in this scenario and identification protocols on mobile platforms are another upcoming aspect that also requires attention to deal on the commercial and social use of identity management system. After all these conclusions, the earlier architecture proposes biometrics as the choice for technology which has been also implemented and described in the earlier architecture. The earlier architecture is designed for mobile devices. This architecture thus acts as an embedded application that provides both verification and identification functionality. It includes identity management to support social activities. Examples of identity management system are finding doubles in a social network. Privacy has been provided by these functionalities which help to overcome the security concern. The architecture of FAMC: Face Authentication for Mobile Concurrence is modular. Functionalities like image acquisition, anti-spoofing, face detection, face segmentation; feature extraction and face matching have been provided by its implementation. The behavior of FAMC allows for recognition and best biometrics sample selection

@article{V_2015, abstract = {It has been observed in the last decades that face recognition has acquired a large amount of attention and curiosity. Benefits of this have been seen in quite a few applications. An architecture which has been implemented earlier addresses the face analysis domain. As compared to other biometrics, face recognition is more advantageous but it is particularly subject to spoofing. The whole cost of the system increases since the accuracy of this technique involves the estimation of the three dimensionality of faces. An effective and efficient solution for face spoofing has been proposed in the paper. The growing use of mobile devices has been a growing concern due to their ability to store and exchange sensitive data. Thus this has given encouragement to the interest of people, to exploit their abilities, from one side, and to protect users from malicious data, on the other side. It is important to develop and deliver secure access in this scenario and identification protocols on mobile platforms are another upcoming aspect that also requires attention to deal on the commercial and social use of identity management system. After all these conclusions, the earlier architecture proposes biometrics as the choice for technology which has been also implemented and described in the earlier architecture. The earlier architecture is designed for mobile devices. This architecture thus acts as an embedded application that provides both verification and identification functionality. It includes identity management to support social activities. Examples of identity management system are finding doubles in a social network. Privacy has been provided by these functionalities which help to overcome the security concern. The architecture of FAMC: Face Authentication for Mobile Concurrence is modular. Functionalities like image acquisition, anti-spoofing, face detection, face segmentation; feature extraction and face matching have been provided by its implementation. The behavior of FAMC allows for recognition and best biometrics sample selection}, added-at = {2015-08-11T08:25:49.000+0200}, author = {Salunkhe, Veena V. and Deshmukh, Mrs. M.P. and Pimplekar, Prabodhi A. and Bhosale, Nikita S. and Bendre, Amruta G.}, biburl = {https://www.bibsonomy.org/bibtex/240e5e9cf6fbea644e8c30bb3aedc1e88/ijritcc}, doi = {10.17762/ijritcc2321-8169.1503106}, interhash = {7d3c11266dd26e8b70764b008e103185}, intrahash = {40e5e9cf6fbea644e8c30bb3aedc1e88}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Biometrics acquisition android anti-spoofing authentication data detection dimensionality encounter extraction face identification identity image malicious management mobile recognition segm spoofing system three verification}, month = {march}, number = 3, pages = {1389--1394}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-11T08:25:49.000+0200}, title = {{FAMC} Face Authentication for Mobile Concurrence}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.1503106}, volume = 3, year = 2015 }