# Twin identification over viewpoint change: A deep convolutional neural network surpasses humans

arXiv: https://arxiv.org/abs/2207.05316 · HTML: https://mlsanity.com/paper/2207.05316/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 71 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [An All-In-One Convolutional Neural Network for Face Analysis](https://mlsanity.com/paper/1611.00851/) (2016)
- [Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed](https://mlsanity.com/paper/1812.10902/) (2018)
- [The Do's and Don'ts for CNN-based Face Verification](https://mlsanity.com/paper/1705.07426/) (2017)
- [Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition](https://mlsanity.com/paper/1804.01159/) (2018)
- [Deep Convolutional Neural Network Features and the Original Image](https://mlsanity.com/paper/1611.01751/) (2016)
- [Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1605.02686/) (2016)

## Built on by (most cited first, 4 of 4)

- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Unlocking the power of partnership: How humans and machines can work together to improve face recognition](https://mlsanity.com/paper/2510.02570/) (2025)
- [Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives](https://mlsanity.com/paper/2605.12619/) (2026)
- [Revisiting Face Recognition for Monozygotic Twins: The Celeb Twins Test Set](https://mlsanity.com/paper/2609.01141/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.