Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset

By Bor-Chun Chen, Chu-Song Chen, Winston H. Hsu. 2015

301 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yu Qiao
2. Stefanos P. Zafeiriou
3. Xiaogang Wang
4. Luc Van Gool
5. Jiankang Deng
6. Dacheng Tao
7. Chen Change Loy
8. Jiashi Feng

In Signal Processing

1. Stefanos P. Zafeiriou
2. Anil K. Jain
3. Yu Qiao
4. Jiankang Deng
5. Yandong Wen
6. Zhenan Sun
7. Zhifeng Li
8. Ran He

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “…Public Data 55K merged no PubFig [31] Celebrity 13K Model generated predictions no IMDB-WIKI [46] IMDB, WIKI 500K no FotW [13] Flickr 25K yes CACD [10] celebrity 160K no DiF [39] Flickr 1M **Skin color prediction † CelebA [37] CelebFace [54, 55] LFW [23] 200K no LFW+ [16] LFW [23 cability of…”
• Diversity in Faces (2019, methodology): “Many other data sets have been proposed for different facial analysis tasks, such as age and gender classification [24, 27, 31, 54–56], facial expression analysis [55], memorability [57], attributes [35] and aging [58].”
• Longitudinal Study of Automatic Face Recognition (2018, methodology): “33) CACD [17] 2,000 163,446 n.”
• Deep Learning for Face Recognition: Pride or Prejudiced? (2019, methodology): “Therefore, in this study we combined face images from two datasets: Adience dataset [12] and Cross-Age Celebrity Dataset (CACD) [10].”
• Past, Present, and Future of Face Recognition: A Review (2020, unknown): “[130] 2016 LeNet 1 Center Loss CASIA WebFace + CACD2000 [154] + Celebrity + [155] (0.”
• Learning Meta Face Recognition in Unseen Domains (2020, unknown): “3 Human, Voting [40] - 94.”
• A Light CNN for Deep Face Representation With Noisy Labels (2015, methodology): “Domain Face Recognition Databases Table VIII(a) shows the results on the CACD-VS dataset. The results of our models on CACD-VS is 98.55% and outperform other age-invariant face recognition algorithms [47], [64], [65] and two open source models [7], [36]. This indicates that our Light CNNs are potent”
• AgeDB: The First Manually Collected, In-the-Wild Age Database (2017, methodology): “In 2014, Cross-Age Celebrity Dataset (CACD) [4, 5] was introduced, containing 163,446 “in-the-wild” images of 2,000 celebrities.”
• A Discriminative Feature Learning Approach for Deep Face Recognition (2016, methodology): “We use the web-collected training data, including CASIAWebFace [39], CACD2000 [4], Celebrity+ [22].”
• Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval (2020, methodology): “The Cross-Age Celebrity Dataset (CACD) [45] consists of 163 , 446 face images belonging to 2 , 000 celebrities.”
• Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition (2016, methodology): “When testing our model on CACD-VS, we remove all the identities in CACD from the training data.”