Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning

Puttemans, Steven; Can, Ergün; Goedemé, Toon

doi:10.5220/0006256003960404

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…It is implemented based on Multi‐scale Block Local Binary Patterns (MB‐LBP) and AdaBoost. MB‐LBP is an extension of traditional LBP by encoding rectangular areas using LBP so that the feature of large‐scale structures can be captured 20 . Figure 6A shows its detected results on the phantom.…”

Section: Methodsmentioning

confidence: 99%

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Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Sun

Hosny

et al. 2022

Robotics Computer Surgery

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Background: Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. Methods:We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm.Results: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. Conclusions:The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

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Section: Methodsmentioning

confidence: 99%

“…MB-LBP is an extension of traditional LBP by encoding rectangular areas using LBP so that the feature of large-scale structures can be captured. 20 Figure 6A shows its detected results on the phantom. The five facial landmarks mentioned above are indexed by 36th, 39th, 42th, 45th and 30th.…”

Section: Facial Landmark Detection In Patient Spacementioning

confidence: 99%

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Sun

Hosny

et al. 2022

Robotics Computer Surgery

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“…For RGB images, we used the already trained cascade classifiers provided by OpenCV 3.3. It provides cascade classifier containing Haar-like features contributed by Howse [ 28 ] and that containing Multi-Block LBP features contributed by Puttemans et al [ 29 ], so we used both of them.…”

Section: Experiments In Real Scenesmentioning

confidence: 99%

Adapting Local Features for Face Detection in Thermal Image

Trung

Uchiyama

et al. 2017

Sensors

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A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

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Application of Active Learning in Carbonate Lithologic Identification

Yuan

Zhang

et al. 2021

2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD)

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Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning

Cited by 6 publications

References 11 publications

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Adapting Local Features for Face Detection in Thermal Image

Application of Active Learning in Carbonate Lithologic Identification

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