Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System Article Swipe
Sandeep Kumar
,
Shilpa Rani
,
Arpit Jain
,
Chaman Verma
,
Maria Simona Raboacă
,
Zoltán Illés
,
Bogdan-Constantin Neagu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145160
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145160
Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.
Related Topics
Concepts
Biometrics
Spoofing attack
Computer science
Face (sociological concept)
Benchmark (surveying)
Facial expression
Expression (computer science)
Artificial intelligence
Facial recognition system
Architecture
Pattern recognition (psychology)
Machine learning
Computer security
Geodesy
Programming language
Visual arts
Geography
Social science
Art
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22145160
- https://www.mdpi.com/1424-8220/22/14/5160/pdf?version=1657607904
- OA Status
- gold
- Cited By
- 113
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285005369
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4285005369Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22145160Digital Object Identifier
- Title
-
Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric SystemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-09Full publication date if available
- Authors
-
Sandeep Kumar, Shilpa Rani, Arpit Jain, Chaman Verma, Maria Simona Raboacă, Zoltán Illés, Bogdan-Constantin NeaguList of authors in order
- Landing page
-
https://doi.org/10.3390/s22145160Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/14/5160/pdf?version=1657607904Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/14/5160/pdf?version=1657607904Direct OA link when available
- Concepts
-
Biometrics, Spoofing attack, Computer science, Face (sociological concept), Benchmark (surveying), Facial expression, Expression (computer science), Artificial intelligence, Facial recognition system, Architecture, Pattern recognition (psychology), Machine learning, Computer security, Geodesy, Programming language, Visual arts, Geography, Social science, Art, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
113Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 26, 2024: 40, 2023: 38, 2022: 9Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Recently, | 27 |
| abstract_inverted_index.benchmark | 169 |
| abstract_inverted_index.biometric | 107 |
| abstract_inverted_index.detection | 130 |
| abstract_inverted_index.essential | 114 |
| abstract_inverted_index.evaluated | 164 |
| abstract_inverted_index.forwarded | 61 |
| abstract_inverted_index.hospitals | 57 |
| abstract_inverted_index.patients, | 51 |
| abstract_inverted_index.spoofing) | 49 |
| abstract_inverted_index.spoofing, | 148, 206 |
| abstract_inverted_index.Expression | 194 |
| abstract_inverted_index.Photograph | 173 |
| abstract_inverted_index.department | 30 |
| abstract_inverted_index.electronic | 22 |
| abstract_inverted_index.expression | 47, 145 |
| abstract_inverted_index.friendlier | 73 |
| abstract_inverted_index.healthcare | 29 |
| abstract_inverted_index.increasing | 7 |
| abstract_inverted_index.paperwork. | 67 |
| abstract_inverted_index.treatments | 86 |
| abstract_inverted_index.Cohn-Kanade | 186 |
| abstract_inverted_index.U-Net-based | 124 |
| abstract_inverted_index.biometrics, | 38 |
| abstract_inverted_index.electronics | 15 |
| abstract_inverted_index.expression. | 217 |
| abstract_inverted_index.information | 111, 140 |
| abstract_inverted_index.methodology | 103, 162 |
| abstract_inverted_index.outperforms | 153 |
| abstract_inverted_index.technology, | 35 |
| abstract_inverted_index.architecture | 125, 133 |
| abstract_inverted_index.modification | 220 |
| abstract_inverted_index.applications. | 244 |
| abstract_inverted_index.Alex-Net-based | 132 |
| abstract_inverted_index.classification | 137 |
| abstract_inverted_index.methodologies. | 159 |
| abstract_inverted_index.soft-biometric | 34, 242 |
| abstract_inverted_index.soft-biometric-based | 4, 102 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5071404767, https://openalex.org/A5055678785 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I141595442, https://openalex.org/I4210148481, https://openalex.org/I61641377, https://openalex.org/I875944469 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile.value | 0.99226109 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |