K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

K. Lüscher, A. Klimczuk, Generations, intergenerational relationships, generational policy: A multilingual compendium, 17 languages, Universität Konstanz, Konstanz 2017, 428pp. TS - BibTeX M4 - Citavi.

本书收录《基督教与道教比较论纲》、《天主教成圣观的天人合一幅度》、《论早期基督宗教的性爱伦理观》等20篇论文。.

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks. However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of (...) three facial expressions. To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp. (shrink)