Wavelet energy feature based source camera identification for ear biometric images

Chowdhury, Debbrota Paul; Bakshi, Sambit; Kumar, Pankaj; Majhi, Banshidhar

doi:10.1016/j.patrec.2018.10.009

Cited by 17 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet analysis is a time-frequency analysis method which processes high frequency resolution. Therefore, each time it is able to keep on decomposing the decomposed low-frequency signal, for purpose of remedying the frequency limitation of the wavelet analysis in high frequency part, the wavelet packet analysis is put forward in the basis of wavelet analysis, which can be described as follows [5][6][7][8][9]…”

Section: Wavelet Packet Analysismentioning

confidence: 99%

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

Yang

Zhang

Zhou

2021

Noise & Vibration Worldwide

View full text Add to dashboard Cite

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair’s sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.

show abstract

Section: Wavelet Packet Analysismentioning

confidence: 99%

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

Yang

Zhang

Zhou

2021

Noise & Vibration Worldwide

View full text Add to dashboard Cite

show abstract

“…This aim can be reached with various algorithm in literature that works in real time, both with Neural Networks and without [14] [15] [16]. The proposed dataset also allows to detect a subject from other biometric traits like ear or iris, as algorithms in [17] [18] performs. This is possible due to the different distance of the subject from the camera during recording.…”

Section: Security Purpose Applicationsmentioning

confidence: 99%

Gotcha-I: A Multiview Human Videos Dataset

Barra

Bisogni

Nappi

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All this trait are useful for a multibiometric identification or a person re-identification in a video surveillance context. Body Worn Cameras (BWCs) are used by the police of various countries all around the word and their use is growing significantly. This raises the need to test new recognition methods that use multibiometric traits on person re-identification. The purpose of this work is to present a new video dataset called Gotcha-I. This dataset has been obtained using more mobile cameras in movement to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environment to address both security and surveillance problem. Subjects have different behavior in videos including walk freely, follow a path, go upstairs, try to avoid the camera, for a total of 493 videos including a 180 degree videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as: the 3D model of the face of each subject with all the poses of the head in pitch, yaw and roll; and the body keypoint coordinates of the gait for each video frame. It's also shown an application of gender recognition performed on Gotcha-I, confirming the usefulness and innovativeness of the proposed dataset.

show abstract

“…Efforts have been made to develop techniques for source identification of medical images [7,9,10,11]. These methods often involve analyzing the image content, statistical properties, or machinespecific patterns to infer the source machine.…”

Section: Introductionmentioning

confidence: 99%

DeepMammo: Deep Learning Algorithm for Digital Mammogram Source Identification

Mohammadi,

Sebro

2024

View full text Add to dashboard Cite

Advances in AI allow for fake image creation. These techniques can be used to fake mammograms. This could impact patient care and medicolegal cases. One method to verify that an image is original is to confirm the source of the image. A deep-learning algorithm(DeepMammo)-based on CNNs and FC-NNs, used to identify the machine that created any mammogram. We analyze mammograms of 1574 patients obtained on 7-different mammography machines and randomly split the dataset by patient into training/validation(80%) and test(20%) datasets. DeepMammo has an accuracy of 98.09%, AUC of 95.96% in the test dataset.

show abstract

Wavelet energy feature based source camera identification for ear biometric images

Cited by 17 publications

References 13 publications

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

Gotcha-I: A Multiview Human Videos Dataset

DeepMammo: Deep Learning Algorithm for Digital Mammogram Source Identification

Contact Info

Product

Resources

About