Ms. Chen Cui Profile

Chen Cui

Masters Student at 4103 Cloud Park Dr Apt B3

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Publications (4)

Proceedings Article | 6 March 2015 Open Access

Recent progress in wide-area surveillance: protecting our pipeline infrastructure

Vijayan Asari, Sidike Paheding, Chen Cui, Varun Santhaseelan

Proceedings Volume 9408, 940802 (2015) https://doi.org/10.1117/12.2086905

KEYWORDS: Image segmentation, Airborne remote sensing, Detection and tracking algorithms, Surveillance, Image processing algorithms and systems, Imaging systems, Image processing, Cameras, Image resolution, Target detection

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Proceedings Article | 6 March 2015 Paper

Robust textural features for real time face recognition

Chen Cui, Vijayan Asari, Andrew Braun

Proceedings Volume 9408, 940806 (2015) https://doi.org/10.1117/12.2083400

KEYWORDS: Facial recognition systems, Feature extraction, Detection and tracking algorithms, Binary data, Video, Cameras, Image enhancement, Image processing, Skin, Light sources and illumination

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Proceedings Article | 3 March 2014 Paper

Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

Chen Cui, Vijayan Asari

Proceedings Volume 9027, 90270L (2014) https://doi.org/10.1117/12.2037846

KEYWORDS: Databases, Facial recognition systems, Light sources and illumination, Feature extraction, Principal component analysis, Amplifiers, Binary data, Eye, Detection and tracking algorithms, Nose

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Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

Proceedings Article | 23 May 2013 Paper

Intrusion detection on oil pipeline right of way using monogenic signal representation

Binu Nair, Varun Santhaseelan, Chen Cui, Vijayan Asari

Proceedings Volume 8745, 87451U (2013) https://doi.org/10.1117/12.2015640

KEYWORDS: Image resolution, Airborne remote sensing, Signal detection, Light sources and illumination, Sensors, Spatial resolution, Detection and tracking algorithms, Image sensors, 3D modeling, Image processing

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