6 research outputs found
Optimization of deep learning features for age-invariant face recognition
This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods
Imbalance Problems in Object Detection: A Review
In this paper, we present a comprehensive review of the imbalance problems in
object detection. To analyze the problems in a systematic manner, we introduce
a problem-based taxonomy. Following this taxonomy, we discuss each problem in
depth and present a unifying yet critical perspective on the solutions in the
literature. In addition, we identify major open issues regarding the existing
imbalance problems as well as imbalance problems that have not been discussed
before. Moreover, in order to keep our review up to date, we provide an
accompanying webpage which catalogs papers addressing imbalance problems,
according to our problem-based taxonomy. Researchers can track newer studies on
this webpage available at:
https://github.com/kemaloksuz/ObjectDetectionImbalance .Comment: Accepted to IEEE TPAMI; currently in pres