# Deep learning for identification and face, gender, expression recognition under constraints

arXiv: https://arxiv.org/abs/2111.01930 · HTML: https://mlsanity.com/paper/2111.01930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [Towards Universal Representation Learning for Deep Face Recognition](https://mlsanity.com/paper/2002.11841/) (2020)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [Recognizing Partial Biometric Patterns](https://mlsanity.com/paper/1810.07399/) (2018)
- [Face Detection using Deep Learning: An Improved Faster RCNN Approach](https://mlsanity.com/paper/1701.08289/) (2017)
- [Multi-view Face Detection Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1502.02766/) (2015)
- [Visual Speech Recognition](https://mlsanity.com/paper/1409.1411/) (2014)
- [Face Recognition: From Traditional to Deep Learning Methods](https://mlsanity.com/paper/1811.00116/) (2018)
- [Deep Face Image Retrieval: a Comparative Study with Dictionary Learning](https://mlsanity.com/paper/1812.05490/) (2018)
- [Partial Face Detection for Continuous Authentication](https://mlsanity.com/paper/1603.09364/) (2016)
- [Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study](https://mlsanity.com/paper/1811.09681/) (2018)
- [Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information](https://mlsanity.com/paper/1309.2752/) (2013)
- [Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection](https://mlsanity.com/paper/1509.07627/) (2015)
- [A PCA-Based Convolutional Network](https://mlsanity.com/paper/1505.03703/) (2015)

## Abstract (arXiv)

Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully connected layers, FC6 and FC7 respectively, in the structure of the VGG19 network provide robust features with each of these two layers containing 4096 features. The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile. Our experimental results indicate that we obtain high accuracy for all the tasks. The best recorded accuracy values are up to 99.95% for identifying persons, 99.9% for gender recognition, 99.9% for age recognition and 80.9% for facial expression (eye smile) recognition.