Improving Facial Landmark Detection via a Super-Resolution Inception Network

Knoche, Martin; Merget, Daniel; Rigoll, Gerhard

doi:10.1007/978-3-319-66709-6_20

Cited by 3 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works have combined SR and object detection algorithms to improve the detection accuracy in various complex scenes. Knoche et al 29 . developed a new SR network architecture to improve the state-of-the-art methods in facial landmark detection.…”

Section: Related Workmentioning

confidence: 99%

“…28 2.3 Object Detection-Based ISR Some works have combined SR and object detection algorithms to improve the detection accuracy in various complex scenes. Knoche et al 29 developed a new SR network architecture to improve the state-of-the-art methods in facial landmark detection. Li et al 30 combined an SR network to enhance bird detection performance in LR images.…”

Section: Object Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Low-resolution object detection via a lightweight super-resolution network

Tang,

Liu,

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

The object detection algorithm has developed rapidly in recent years and achieved excellent results on various benchmark datasets. However, these datasets are usually composed of high-quality [high resolution (HR), high signal-to-noise ratio, etc.] images. In many scenarios, we need to detect objects in low-resolution (LR) images. But the detector trained in HR images performs poorly in LR images. The image super-resolution (SR) algorithm is an important image processing technology that has been proven to improve the performance of various visual tasks. Based on this consideration, we combine image SR technology with the object detection task to design an object detection algorithm for detecting LR images. Specifically, we propose a lightweight SR algorithm that achieves a good balance between parameters and performance. We perform SR reconstruction on the LR image in advance and detect the reconstructed image instead of directly detecting the LR image. Both quantitative and qualitative experimental results show that our LR object detection algorithm significantly improves the detection performance of LR images.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Object Detectionmentioning

confidence: 99%

Low-resolution object detection via a lightweight super-resolution network

Tang,

Liu,

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…The images of the training set are first cropped based on the face bounding box provided by the database. Knoche et al [43] researched the effect of the image resolution on performance of facial landmark prediction and found that there was a decline of performance when the image resolution is smaller than 50×50 px. We thus, resize all the cropped face images to be an equal size of 96×96px.…”

Section: A Preprocessingmentioning

confidence: 99%

Accurate and robust eye center localization via fully convolutional networks

Xia

Wang

2019

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an endto-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

show abstract