TY - GEN
T1 - ASF-LLRDA
T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
AU - Widyadhana, Arya
AU - Hidayati, Shintami Chusnul
AU - Navastara, Dini Adni
AU - Anistyasari, Yeni
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Face recognition is a crucial task in numerous applications, but it has difficulties because of the high-dimensional nature of facial photos, limited sample sizes, variations in illumination, and facial expressions. This paper presents a novel face recognition approach to overcome these challenges by combining the advantages of the Approximately Symmetrical Face (ASF) preprocessing strategy and the Locality-regulated Linear Regression Discriminant Analysis (LLRDA) method. The proposed method, called ASF-LLRDA, makes use of ASF to generate axis-symmetric face images for reducing the impact of illumination and variations in facial expressions, followed by LLRDA to extract discriminative features and project the data into a lower dimensional space. Locality-regulated Linear Regression (LLRC) is further utilized as the classifier. Experimental results on the Yale-B face dataset demonstrated the superiority of the proposed method compared to the baselines.
AB - Face recognition is a crucial task in numerous applications, but it has difficulties because of the high-dimensional nature of facial photos, limited sample sizes, variations in illumination, and facial expressions. This paper presents a novel face recognition approach to overcome these challenges by combining the advantages of the Approximately Symmetrical Face (ASF) preprocessing strategy and the Locality-regulated Linear Regression Discriminant Analysis (LLRDA) method. The proposed method, called ASF-LLRDA, makes use of ASF to generate axis-symmetric face images for reducing the impact of illumination and variations in facial expressions, followed by LLRDA to extract discriminative features and project the data into a lower dimensional space. Locality-regulated Linear Regression (LLRC) is further utilized as the classifier. Experimental results on the Yale-B face dataset demonstrated the superiority of the proposed method compared to the baselines.
UR - http://www.scopus.com/inward/record.url?scp=85180010386&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC58517.2023.10317162
DO - 10.1109/APSIPAASC58517.2023.10317162
M3 - Conference contribution
AN - SCOPUS:85180010386
T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
SP - 2031
EP - 2036
BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 October 2023 through 3 November 2023
ER -