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- Alex Chichung KotSuggested from co-authorship
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Works66 from public data
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- Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism66
A preliminary non-intrusive approach based on skeleton keypoint identification using pretrained deep neural networks on human body video clips to extract features and perform body movement analysis that differentiates typical and atypical behaviors of children is proposed.
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- Deep residual network with regularised fisher framework for detection of melanoma52
A deep convolutional neural network-based regularised discriminant learning framework which extracts low-dimensional discriminative features for melanoma detection is proposed and minimises the whole of within-class variance information and maximises the total class variance information.
- Egocentric activity recognition with multimodal fisher vector47
A novel strategy to extract temporal trajectory-like features from sensor data and propose to apply the Fisher Kernel framework to fuse video and temporal enhanced sensor features to enhance information-rich video data is presented.
- A Wearable Face Recognition System on Google Glass for Assisting Social Interactions47
This paper proposes a wearable face recognition system on Google Glass, which acts as a social interaction assistant, the application includes face detection, eye localization, face recognition and a user interface for personal information display.
- Complete discriminant evaluation and feature extraction in kernel space for face recognition40
This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples.
- Multi-sensor Self-Quantification of Presentations34
This work proposes a novel multi-sensor self-quantification framework for presentations and conducts a user study with the speakers to verify the effectiveness of the system generated analytics, which has received positive and promising feedback.
- Enhanced maximum likelihood face recognition31
A method to enhance maximum likelihood face recognition is presented, which selects a more robust weighting parameter and discards unreliable dimensions to circumvent problems of the unreliable small and zero eigenvalues.
- Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma29
A deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization is formulated, which is found to achieve enhanced R NFLD detection performance with sensitivity and false positive per image on a newly created early glaucomatic fundus image database.
- Performance evaluation of local descriptors and distance measures on benchmarks and first-person-view videos for face identification27
Comparative analysis on these databases using various descriptors shows the superiority of BSIF with Cosine, Chi square and Cityblock distance measures using 1-NN as classifier over other descriptors and distance measures and even some of the current state-of-art benchmark database results.
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- Prediction of eigenvalues and regularization of eigenfeatures for human face verification23
Experimental results on popular face databases show that the proposed prediction and regularization strategy for alleviating the conventional problems of LDA and its variants consistently outperforms others.
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- GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment20
Experimental results showing better or comparable segmentation performance and decrease in cup-to-disc height ratio estimation errors demonstrate the efficacy of the proposed GlaucoNet-based segmentation methodology.
- Variance-guided attention-based twin deep network for cross-spectral periocular recognition20
Ablation studies and experimental results on three publicly available cross-spectral periocular datasets containing images from VIS, near-infrared (NIR), and night vision domains show that the proposed deep network achieves the state-of-the-art recognition performances on all three datasets.
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- Exploring users' attitudes towards social interaction assistance on Google Glass19
It is found that users in general appreciated the usefulness of wearable assistance for social interactions and highlighted a few key issues of how wearable devices affect user experience in social interaction.
- Deep Adaptive Temporal Pooling for Activity Recognition18
A learnable and differentiable module: Deep Adaptive Temporal Pooling (DATP), which applies a self-attention mechanism to adaptively pool the classification scores of different video segments to improve training of frame-level feature extractor.
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- Face recognition: Perspectives from the real world16
The gaps between expectations of the user and what the system can deliver are discussed and the existing algorithms with modifications are evaluated for applications including FR on wearable devices (like Google Glass) for improving social interactions.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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