Dr. Shufen Cao Profile

Dr. Shufen Cao

at Case Western Reserve Univ

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 March 2024 Poster + Paper

Deep learning-based patient in-position detection for acupuncture treatment

Pu Zhao, Liaojun Pang, Shufen Cao, Zhicheng Cao

Proceedings Volume 12857, 128570R (2024) https://doi.org/10.1117/12.3023364

KEYWORDS: Object detection, Head, Data modeling, Performance modeling, Neck, Medicine, Evolutionary algorithms, Data acquisition, Cameras, Deep learning

Read Abstract +

As a traditional Chinese medicine practice, acupuncture has long been shown to benefit pain and stress relief (especially for elderly people with chronical cases). Therefore, acupuncture is an important and effective alternative medical therapy for disabled elderly population living in areas of low healthcare coverage, which has become a more and more serious social problem as the Chinese population ages rapidly. However, training of acupuncturists is quite expensive and time consuming. With the arrival of the era of AI, how to automate the process of acupuncture treatment and minimalize the involvement of human labor has emerged as a great challenge and opportunity. This research studies a prerequisite of automatic acupuncture treatment: patient in-position detection during the acupuncture treatment process. We propose a fast and accurate one-stage anchor-free DNN model for patient in-position detection. Our model is an improvement of the basis model, YOLO X. The proposed framework consists of a backbone of CSP-DarkNet, a neck of feature pyramid network and a Decoupled Head. As for loss function, we combine the CIoU and the alpha-IoU losses to inherit both their advantages. A simplified version of the advanced label assignment technique of OTA, as well as data augmentation strategies of Mosaic and MixUp are utilized to improve the algorithm performance. Results on a self-collected dataset of acupuncture treatment (named as ATPD Dataset) show that our algorithm significantly outperform other state-of-the-art methods in the literature that are either multiple-staged or single-staged.

Proceedings Article | 2 August 2021 Presentation + Paper

Face detection in the darkness using infrared imaging: a deep-learning-based study

Zhicheng Cao, Heng Zhao, Shufen Cao, Liaojun Pang

Proceedings Volume 11843, 118430K (2021) https://doi.org/10.1117/12.2597194

KEYWORDS: Facial recognition systems, Infrared imaging, Infrared detectors, Thermography, Performance modeling, Sensors, Detection and tracking algorithms

Read Abstract +

Face detection is one of the most important research topics in the field of computer vision, and it is also the premise and an essential part of face recognition. With the advent of deep learning-based techniques, the performance of face detection has been largely improved and more and more daily applications have been witnessed. However, face detection is greatly affected by environmental illumination. Most of existing face detection algorithms neglect harsh illumination conditions such as nighttime condition where lighting is insufficient or it is totally dark. These conditions are often encountered in real-world scenarios, e.g., nighttime surveillance in law enforcement or civil settings. How to overcome the problem of face detection in the darkness becomes a critical and urgent demand. We thus in this paper study face detection in the darkness using infrared (IR) imaging. We build an IR face detection dataset and design a deep learning-based model to study the face detection performance. Specifically, the deep learning model is a Single Stage Detector which has the advantage of fast speed and lower computation cost compared with other face detectors that consists of multiple stages. In the experiment, we also compare the performance of our deep learning model with that of a well-known traditional face detection algorithm, AdaBoost. In terms of True Positive Rate (TPR), our model significantly outperforms AdaBoost by 5% -- a dramatic boost from 87% to 92%, which suggests our deep learning-based method with IR imaging can indeed meet the requirement of real-world nighttime face detection applications.

Proceedings Article | 2 August 2021 Presentation + Paper

Improving nighttime periocular recognition performance by image superresolution

Zhicheng Cao, Xing Chen, Shufen Cao, Liaojun Pang

Proceedings Volume 11843, 118430L (2021) https://doi.org/10.1117/12.2598442

KEYWORDS: Super resolution, Image quality, Infrared imaging, Visible radiation, Near infrared, Image resolution, Principal component analysis, Iris recognition, Facial recognition systems, Neural networks

Read Abstract +

Recognition of individual identity using the periocular region (i.e., periocular recognition) has emerged as a relatively new modality of biometrics and is a potential substitute for face recognition when facial occlusion happens, e.g., when wearing a mask. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance in law reinforcement. We therefore study the topic of periocular recognition at nighttime using the infrared spectrum. However, the useful and effective area for periocular recognition is quite limited compared to that of face recognition since only the eyes are exposed. As a result, the performance of periocular recognition algorithms is relatively low. This issue of limited area poses a serious challenge even though many state-of-the-art face recognition algorithms yield high performance. This situation is even more deteriorated when periocular recognition is performed at nighttime. Thus, we in this paper propose an image super-resolution (SR) based technique for nighttime periocular recognition in which we enlarge the small-sized periocular image to have a larger effective area while retaining a high image quality. Super-resolution of the periocular images is achieved by a CNN model which first conducts interpolation of the periocular area to an expected size and then finds a nonlinear mapping between the input low quality periocular image and the output high quality periocular image. To validate our method, we compare our deep learning-based SR method with the original case of none SR involved at all, as well as the other two cases using traditional SR methods, namely bilinear interpolation and bicubic interpolation. In terms of quality metrics such as PSNR and SSIM as well as recognition metrics such as GAR and EER, our method significantly outperforms all the other three methods.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years