I-Am: Implicitly Authenticate Me Person Authentication on Mobile Devices Through Ear Shape and Arm Gesture
Product Description
Biometric based person authentication and identification have become common practices in many contexts, and their diffusion is expected to steadily grow in the next years also thanks to the diffusion of the latest generation of mobile devices equipped with a plethora of accurate and reliable sensors along with more and more powerful processors. In the existing system, we conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system’s overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. In the proposed system, a new novel model is created using the face feature extraction technique which is the hybrid combination of existing Voila-Jones model and artificial neural network for feature detection model. The proposed technique focused on detecting the uniqueness present in the face reactions and the manipulation of ear piece while laughing etc. This proposed model we feel the authenticate and accurate system for detecting the uniqueness present in the input images.
Proposed system
In the proposed system, a new novel model is created using the face feature extraction technique which is the hybrid combination of existing Advanced Principle component analysis and artificial neural network for feature detection model. The proposed technique focused on detecting the uniqueness present in the voice data and the manipulation of ear piece etc. This proposed model we feel the authenticate and accurate system for detecting the uniqueness present in the input images.
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