Discrepancies　between　the　estimated　brain　age　from　brain　structural　MRI　and　the　chronological　age　have　been　associated　with　a　broad　spectrum　of　neurocognitive　disorders.　The　performance　of　brain　age　estimation　heavily　depends　on　predefined　or　hand-crafted　features.　Although　3D　convolutional　neural　network　(CNN)　based　approaches　have　been　proposed,　they　require　high　computational　cost,　large　memory　load,　and　numerous　images.　Coupling　a　pre-trained　2D　CNN　for　transfer　learning　with　a　well-established　relevance　vector　machine　for　regression　approach　can　greatly　enhance　the　capabilities　of　the　model.　Several　important　strategies,　including　feature　transfer　learning,　3D　feature　concatenation,　and　dimensionality　reduction　were　taken.　The　estimated　brain　age　was　modeled　by　structural　magnetic　resonance　imaging　(sMRI)　from　594　normal　healthy　older　individuals　(age　50-90　years).　We　proposed　and　manifested　a　pre-trained　AlexNet　as　a　robust　feature　extractor.　Also,　the　considerable　cost　of　developing　a　3D　CNN　was　avoided　by　applying　3D　feature　concatenation　and　data　reduction.　The　proposed　method　achieves　superior　performance　with　a　mean　absolute　error　of　4.51　years　for　old　subjects.　The　predicted　brain　age　also　demonstrated　high　test-retest　reliability　(intra-class　correlation　coefficient　of　0.979).　The　effectiveness　and　robustness　of　the　proposed　model　were　well　studied.　The　proposed　approach　can　compete　with　or　even　outperform　those　state-of-the-art　approaches,　and　feature　transfer　learning　strategy　can　introduce　new　perspectives　to　some　well-established　brain　age　prediction　models　with　predefined　or　hand-crafted　features.