Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations

Shi, Wenjing; Pattichis, Marios S.; Celedón‐Pattichis, Sylvia; LópezLeiva, Carlos

doi:10.1109/ssiai.2018.8470355

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…Additional references for the AM-FM theory were used from [13]. Also, regarding the Gabor filterbank with DCA, AM-FM methods has been used as a feature extraction for head and hair detection in group interactions, refer to [10] and [11].…”

Section: Am-fm Demodulationmentioning

confidence: 99%

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

Sanchez

Pattichis

Celedón‐Pattichis

et al. 2020

2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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Large scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed.This work examines the impact of using AM-FM representations as input images for CNN classification applications. A comparison was made between AM-FM components combinations and gray scale images as inputs for reduced and complete networks.The results showed that only the phase component produced significant predictions within a simple network. Neither IA or gray scale image were able to induce any learning in the system. Furthermore, the FM results were 7x faster during training and used 123x less parameters compared to state-of-the-art MobileNetV2 architecture, while maintaining comparable performance (AUC of 0.78 vs 0.79).

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Section: Am-fm Demodulationmentioning

confidence: 99%

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

Sanchez

Pattichis

Celedón‐Pattichis

et al. 2020

2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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“…The current paper dramatically extends this prior research that was focused on very short video datasets without considering occlusion, appearance issues, and associating hands with different people. We also note that head detection and person recognition has been studied in [14], [15], [19] and [17]. Human activity classification over cropped regions was studied in [5], [16] and [8].…”

Section: Introductionmentioning

confidence: 99%

Fast Hand Detection in Collaborative Learning Environments

Teeparthi¹,

Jatla²,

Pattichis³

et al. 2021

Preprint

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Long-term object detection requires the integration of framebased results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance. Our approach integrates object-detection, followed by time projections, clustering, and small region removal to provide effective hand detection over long videos. The hand detector achieved average precision (AP) of 72% at 0.5 intersection over union (IoU). The detection results were improved to 81% by using our optimized approach for data augmentation. The method runs at 4.7×the real-time with AP of 81% at 0.5 intersection over the union. Our method reduced the number of false-positive hand detections by 80% by improving IoU ratios from 0.2 to 0.5. The overall hand detection system runs at 4× real-time.

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“…In [4], the authors introduced methods for detecting writing, typing, and talking activities using motion vectors and deep learning. In [6], the authors developed methods to detect where participants were looking at. In [7], the authors demonstrated that FM images with low-complexity neural networks can provide face detection results that can only be achieved with much more complex deep learning systems.…”

Section: Introductionmentioning

confidence: 99%

Facial Recognition in Collaborative Learning Videos

Tran¹,

Pattichis²,

Celedón‐Pattichis³

et al. 2021

Preprint

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Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate methods. We develop a dynamic system of recognizing participants in collaborative learning systems. We address occlusion and recognition failures by using past information about the face detection history. We address the need for detecting faces from different poses and the need for speed by associating each participant with a collection of prototype faces computed through sampling or K-means clustering. Our results show that the proposed system is proven to be very fast and accurate. We also compare our system against a baseline system that uses InsightFace [2] and the original training video segments. We achieved an average accuracy of 86.2% compared to 70.8% for the baseline system. On average, our recognition rate was 28.1 times faster than the baseline system.

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Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations

Cited by 11 publications

References 19 publications

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

Fast Hand Detection in Collaborative Learning Environments

Facial Recognition in Collaborative Learning Videos

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