A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic

Abstract

The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of bio-metric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data.

A digital signature system based on hand geometry - Survey

“…The accuracy recognition rate for the matching were 88% for CNN with 9.98 % EER, 95% for FFBB with 9.99 % EER, and 92% for Elman with 9.97 % EER. And also in the same year , the authors [23], a proposed design framework for the hand geometry system is implemented in MATLAB environment. The first step is to extract images from the database using pre-image technology.…”

“…It is also possible to change the brightness using geometric transformation methods [11]. A morphological filter is employed to fill-in all small gaps to blacken the isolated white pixels and to reduce the blurring of edges [20] [23]. Some preprocessing applied in order to remove the effects of shadow and light by enhancement filters that applied to image [18], also Erosion removes the unwanted part dots or pixels.…”

“…At different points of the finger, more than one measurement can be taken for the width. The length of the lines on the finger can also be used as a measure of the width of the finger, Area of the largest inscribed circle, Angels, thickness (with respect to the area of the hand) [16] [10] [14] [22] [18] [23].…”

“…Eight datasets contained 29,337 RGB hand images in an egocentric view at 640 × 480 resolution. The CASIA hand geometry image database was used in standard databases [9] [23]. The dataset was collected from 100 people.…”

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

“…This work offers a low-cost authentication system. Doroz et al [23] had extracted biometric characteristics of hand-image and neural network based on healing mechanism is applied to perform recognition. However, this method is not cost effective as it requires extensive learning parameters and a controlled environment.…”