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In this paper, we investigate the reliability of online recognitionplatforms, Amazon Rekognition and Microsoft Azure, with respect to changes inbackground, acquisition device, and object orientation. We focus on platformsthat are commonly used by the public to better understand their real-worldperformances. To assess the variation in recognition performance, we perform acontrolled experiment by changing the acquisition conditions one at a time. Weuse three smartphones, one DSLR, and one webcam to capture side views andoverhead views of objects in a living room, an office, and photo studio setups.Moreover, we introduce a framework to estimate the recognition performance withrespect to backgrounds and orientations. In this framework, we utilize bothhandcrafted features based on color, texture, and shape characteristics anddata-driven features obtained from deep neural networks. Experimental resultsshow that deep learning-based image representations can estimate therecognition performance variation with a Spearman's rank-order correlation of0.94 under multifarious acquisition conditions.

Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation