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Grant support

This work was supported by the Spanish Ministry of Economy and Competitiveness, project TIN2016-75982-C2-2-R. Jose M. Buenaposada was also partially funded by the Comunidad de Madrid project RoboCity2030-DIH-CM (S2018/NMT-4331). The authors would like to thank the anonymous reviewers for their comments and Felix Kuhnke for his help in interpreting Biwi annotations.

Analysis of institutional authors

Valle, RAuthorBaumela, LAuthor

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Article

Multi-Task Head Pose Estimation in-the-Wild

Publicated to:Ieee Transactions On Pattern Analysis And Machine Intelligence. 43 (8): 2874-2881 - 2021-08-01 43(8), DOI: 10.1109/TPAMI.2020.3046323

Authors: Valle, Roberto; Buenaposada, Jose M; Baumela, Luis

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Abstract

We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-decoder CNN with residual blocks and lateral skip connections. We show that the combination of head pose estimation and landmark-based face alignment significantly improve the performance of the former task. Further, the location of the pose task at the bottleneck layer, at the end of the encoder, and that of tasks depending on spatial information, such as visibility and alignment, in the final decoder layer, also contribute to increase the final performance. In the experiments conducted the proposed model outperforms the state-of-the-art in the face pose and visibility tasks. By including a final landmark regression step it also produces face alignment results on par with the state-of-the-art.

Keywords

AlgorithmAlgorithmsAlignmentDecodingDeep learningEncoder-decoderFaceFace alignmentFace poseFace recognitionFacesHead pose estimationImage processingImage processing, computer-assistedMagnetic headsMulti-task learningNetwork architectureNeural networks, computerOcclusions detectionPose estimationSignal encodingSpatial informationsState of the artTask analysisTrainingTraining strategyVisibility

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Transactions On Pattern Analysis And Machine Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 2/276, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.79. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.38 (source consulted: FECYT Feb 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-31, the following number of citations:

  • WoS: 37
  • Scopus: 56
  • Europe PMC: 3
  • OpenCitations: 49

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-31:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 65.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 65 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.75.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (VALLE FERNáNDEZ, ROBERTO) and Last Author (BAUMELA MOLINA, LUIS).